{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi step feed-forward neural network model (vector output approach)\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training a feed-forward neural network forecasting model\n",
    "- get data in the required shape for the keras API\n",
    "- implement a feed-forward NN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of temperature and load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n",
    "- enable early stopping to reduce the likelihood of model overfitting\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please run this notebook after completing 0_data_setup notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from glob import glob\n",
    "from collections import UserDict\n",
    "from common.utils import *\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data into Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "      <td>32.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load  temp\n",
       "2012-01-01 00:00:00 2,698.00 32.00\n",
       "2012-01-01 01:00:00 2,558.00 32.67\n",
       "2012-01-01 02:00:00 2,444.00 30.00\n",
       "2012-01-01 03:00:00 2,402.00 31.00\n",
       "2012-01-01 04:00:00 2,403.00 32.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '../data/'\n",
    "energy = load_data(data_dir)\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we will set T=6. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of T=6 was arbitrary but should be selected through experimentation.  \n",
    "\n",
    "HORIZON=3 specifies that we have a forecasting horizon of 3 (t+1, t+2, t+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/multistep_forecast.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 6\n",
    "HORIZON = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "y_scaler = MinMaxScaler()\n",
    "y_scaler.fit(train[['load']])\n",
    "\n",
    "X_scaler = MinMaxScaler()\n",
    "train[['load', 'temp']] = X_scaler.fit_transform(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the TimeSeriesTensor convenience class to:\n",
    "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "2. Discard any samples with missing values\n",
    "3. Transform this Pandas dataframe into a numpy array of shape (samples, features) for input into Keras\n",
    "\n",
    "The class takes the following parameters:\n",
    "\n",
    "- **dataset**: original time series\n",
    "- **target** name of the target column\n",
    "- **H**: the forecast horizon\n",
    "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n",
    "- **freq**: time series frequency (default 'H' - hourly)\n",
    "- **drop_incomplete**: (Boolean) whether to drop incomplete samples (default True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n",
    "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>tensor</th>\n",
       "      <th colspan=\"3\" halign=\"left\">target</th>\n",
       "      <th colspan=\"12\" halign=\"left\">X</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th colspan=\"3\" halign=\"left\">y</th>\n",
       "      <th colspan=\"6\" halign=\"left\">load</th>\n",
       "      <th colspan=\"6\" halign=\"left\">temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time step</th>\n",
       "      <th>t+1</th>\n",
       "      <th>t+2</th>\n",
       "      <th>t+3</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tensor              target              X                                     \\\n",
       "feature                  y           load                          temp        \n",
       "time step              t+1  t+2  t+3  t-5  t-4  t-3  t-2  t-1    t  t-5  t-4   \n",
       "2012-01-01 05:00:00   0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43   \n",
       "2012-01-01 06:00:00   0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40   \n",
       "2012-01-01 07:00:00   0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41   \n",
       "\n",
       "tensor                                   \n",
       "feature                                  \n",
       "time step            t-3  t-2  t-1    t  \n",
       "2012-01-01 05:00:00 0.40 0.41 0.42 0.41  \n",
       "2012-01-01 06:00:00 0.41 0.42 0.41 0.40  \n",
       "2012-01-01 07:00:00 0.42 0.41 0.40 0.39  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs.dataframe.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23368, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_inputs.dataframe.as_matrix()[:,HORIZON:]\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18, 0.23, 0.29],\n",
       "       [0.23, 0.29, 0.35],\n",
       "       [0.29, 0.35, 0.37],\n",
       "       ...,\n",
       "       [0.61, 0.58, 0.51],\n",
       "       [0.58, 0.51, 0.43],\n",
       "       [0.51, 0.43, 0.34]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct validation set (keeping W hours from the training set in order to construct initial features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1461, 12)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n",
    "valid[['load', 'temp']] = X_scaler.transform(valid)\n",
    "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n",
    "X_valid = valid_inputs.dataframe.as_matrix()[:,HORIZON:]\n",
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement feed-forward neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement feed-forward neural network with the following structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/ff_multi_step_vector_output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "LATENT_DIM = 5\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(LATENT_DIM, activation=\"relu\", input_shape=(2*T,)))\n",
    "model.add(Dense(HORIZON))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 5)                 65        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 18        \n",
      "=================================================================\n",
      "Total params: 83\n",
      "Trainable params: 83\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the early stopping criteria. We monitor the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by min_delta after patience epochs, we stop the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_val = ModelCheckpoint('model_{epoch:02d}.h5', save_best_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23368 samples, validate on 1461 samples\n",
      "Epoch 1/50\n",
      "23368/23368 [==============================] - 1s 47us/step - loss: 0.1011 - val_loss: 0.0097\n",
      "Epoch 2/50\n",
      "23368/23368 [==============================] - 1s 25us/step - loss: 0.0087 - val_loss: 0.0071\n",
      "Epoch 3/50\n",
      "23368/23368 [==============================] - 1s 26us/step - loss: 0.0060 - val_loss: 0.0047\n",
      "Epoch 4/50\n",
      "23368/23368 [==============================] - 1s 36us/step - loss: 0.0047 - val_loss: 0.0047\n",
      "Epoch 5/50\n",
      "23368/23368 [==============================] - 1s 29us/step - loss: 0.0042 - val_loss: 0.0048\n",
      "Epoch 6/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0041 - val_loss: 0.0064\n",
      "Epoch 7/50\n",
      "23368/23368 [==============================] - 0s 20us/step - loss: 0.0040 - val_loss: 0.0038\n",
      "Epoch 8/50\n",
      "23368/23368 [==============================] - 1s 31us/step - loss: 0.0039 - val_loss: 0.0037\n",
      "Epoch 9/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0039 - val_loss: 0.0048\n",
      "Epoch 10/50\n",
      "23368/23368 [==============================] - 1s 32us/step - loss: 0.0038 - val_loss: 0.0038\n",
      "Epoch 11/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0038 - val_loss: 0.0045\n",
      "Epoch 12/50\n",
      "23368/23368 [==============================] - 1s 23us/step - loss: 0.0038 - val_loss: 0.0062\n",
      "Epoch 13/50\n",
      "23368/23368 [==============================] - 1s 22us/step - loss: 0.0037 - val_loss: 0.0034\n",
      "Epoch 14/50\n",
      "23368/23368 [==============================] - 1s 26us/step - loss: 0.0037 - val_loss: 0.0036\n",
      "Epoch 15/50\n",
      "23368/23368 [==============================] - 1s 34us/step - loss: 0.0037 - val_loss: 0.0047\n",
      "Epoch 16/50\n",
      "23368/23368 [==============================] - 1s 34us/step - loss: 0.0037 - val_loss: 0.0037\n",
      "Epoch 17/50\n",
      "23368/23368 [==============================] - 0s 18us/step - loss: 0.0036 - val_loss: 0.0047\n",
      "Epoch 18/50\n",
      "23368/23368 [==============================] - 0s 18us/step - loss: 0.0036 - val_loss: 0.0033\n",
      "Epoch 19/50\n",
      "23368/23368 [==============================] - 1s 26us/step - loss: 0.0036 - val_loss: 0.0033\n",
      "Epoch 20/50\n",
      "23368/23368 [==============================] - 1s 33us/step - loss: 0.0036 - val_loss: 0.0039\n",
      "Epoch 21/50\n",
      "23368/23368 [==============================] - 1s 26us/step - loss: 0.0036 - val_loss: 0.0032\n",
      "Epoch 22/50\n",
      "23368/23368 [==============================] - 1s 22us/step - loss: 0.0036 - val_loss: 0.0032\n",
      "Epoch 23/50\n",
      "23368/23368 [==============================] - 1s 35us/step - loss: 0.0035 - val_loss: 0.0032\n",
      "Epoch 24/50\n",
      "23368/23368 [==============================] - 1s 31us/step - loss: 0.0035 - val_loss: 0.0041\n",
      "Epoch 25/50\n",
      "23368/23368 [==============================] - 1s 31us/step - loss: 0.0035 - val_loss: 0.0035\n",
      "Epoch 26/50\n",
      "23368/23368 [==============================] - 1s 25us/step - loss: 0.0035 - val_loss: 0.0031\n",
      "Epoch 27/50\n",
      "23368/23368 [==============================] - 1s 27us/step - loss: 0.0035 - val_loss: 0.0035\n",
      "Epoch 28/50\n",
      "23368/23368 [==============================] - 0s 21us/step - loss: 0.0035 - val_loss: 0.0044\n",
      "Epoch 29/50\n",
      "23368/23368 [==============================] - 1s 32us/step - loss: 0.0035 - val_loss: 0.0044\n",
      "Epoch 30/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0035 - val_loss: 0.0036\n",
      "Epoch 31/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0035 - val_loss: 0.0031\n",
      "Epoch 32/50\n",
      "23368/23368 [==============================] - 1s 26us/step - loss: 0.0035 - val_loss: 0.0037\n",
      "Epoch 33/50\n",
      "23368/23368 [==============================] - 1s 24us/step - loss: 0.0034 - val_loss: 0.0030\n",
      "Epoch 34/50\n",
      "23368/23368 [==============================] - 1s 27us/step - loss: 0.0034 - val_loss: 0.0041\n",
      "Epoch 35/50\n",
      "23368/23368 [==============================] - 0s 18us/step - loss: 0.0034 - val_loss: 0.0030\n",
      "Epoch 36/50\n",
      "23368/23368 [==============================] - 0s 21us/step - loss: 0.0034 - val_loss: 0.0040\n",
      "Epoch 37/50\n",
      "23368/23368 [==============================] - 1s 22us/step - loss: 0.0034 - val_loss: 0.0030\n",
      "Epoch 38/50\n",
      "23368/23368 [==============================] - 1s 33us/step - loss: 0.0034 - val_loss: 0.0032\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,\n",
    "                    train_inputs['target'],\n",
    "                    batch_size=BATCH_SIZE,\n",
    "                    epochs=EPOCHS,\n",
    "                    validation_data=(X_valid, valid_inputs['target']),\n",
    "                    callbacks=[earlystop, best_val],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the model with the smallest mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_epoch = np.argmin(np.array(history.history['val_loss']))+1\n",
    "model.load_weights(\"model_{:02d}.h5\".format(best_epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1456, 12)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "test = energy.copy()[test_start_dt:][['load', 'temp']]\n",
    "test[['load', 'temp']] = X_scaler.transform(test)\n",
    "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n",
    "X_test = test_inputs.dataframe.as_matrix()[:,HORIZON:]\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22, 0.28, 0.34],\n",
       "       [0.3 , 0.35, 0.39],\n",
       "       [0.37, 0.42, 0.44],\n",
       "       ...,\n",
       "       [0.61, 0.54, 0.53],\n",
       "       [0.56, 0.47, 0.4 ],\n",
       "       [0.52, 0.45, 0.35]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 05:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,679.23</td>\n",
       "      <td>2,714.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 06:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,953.32</td>\n",
       "      <td>2,970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 07:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,184.91</td>\n",
       "      <td>3,189.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 08:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,352.86</td>\n",
       "      <td>3,356.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 09:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,444.02</td>\n",
       "      <td>3,436.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 05:00:00  t+1    2,679.23 2,714.00\n",
       "1 2014-11-01 06:00:00  t+1    2,953.32 2,970.00\n",
       "2 2014-11-01 07:00:00  t+1    3,184.91 3,189.00\n",
       "3 2014-11-01 08:00:00  t+1    3,352.86 3,356.00\n",
       "4 2014-11-01 09:00:00  t+1    3,444.02 3,436.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute MAPE for each forecast horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h\n",
       "t+1   0.02\n",
       "t+2   0.04\n",
       "t+3   0.06\n",
       "Name: APE, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "eval_df.groupby('h')['APE'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute MAPE across all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037177559293456945"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oMVd1yx/8IAhNFJqLbLZWIHqIQIw2YYH4trfB/v3mcalkzl24CIoQPTyBCEFotycQq1X4+tclD/FcEIEoCILQAmSztQIx3AdRawkzjTIzHUSAkRF4+un6j0U4f6amghBTD62lF2nUCV+HTzwBHR3B80zGXJNCdAkXhrIs2LbNbJR6nDhhojOEpSECsUFMTExw5513Lvn4D37wg2zfvp1t27bx3ve+l6yUJBQEIcRMB9GyoL09eN9xRCBGlfDCNOz6/uQnIiqakUwmuBbDyLmMLuFiQo4D3/0udHdDLBa8NjzcuPEJizOzcvD115sWFx5aSx7iuSACsUHMJxDvvvtuPvWpT816/XOf+xwvvvgiL730EhdffDFf/OIX6zBKQRCahclJEwoVbnGxZo15z3MQJcQ0moQF4uWXw7p15nGpBP/2b40Zk/DaCQsJz8HQWiphRpnwNXjggNlMU6rWgRobkxDFKBMO61YK4nG48MLgfccx51BYGrFGDyAK7Ny5Mj/3llvmf++Tn/wkhw4dYseOHbz97W/ns5/97II/q7u7GwCtNfl8HhWuPiEIwqonna4tUNPVZSZIDwkxjS7hHMS2NnjnO+GrXzXhUM8+CxddBL/wC40bn3BueKGIWhsHynOmJPAnungCsVw2xWkGBszzsKNfKplz2NVV//EJi+NVggbT4iKZNNdeuB+wCMSlIwKxQdxxxx3s2bOH3bt3L/l7PvCBD/CDH/yAq666ir/7u79bwdEJgtBseK6FF2La21u72+044iBGlbB7kUzC5s0wNAS7dpnXPvEJePzx2sWqEE3CLoXn5nuIgxhdvGtw//75Q769cysCMZqEHcREAvr7zf00PA+KQFw6IhBZ2OmrB6Ojo9x6660AjI2NUSqVuP/++wG45557uNYNov7nf/5nqtUqf/zHf8w3vvENPvCBDzRszIIgRAuvCbAnENesCRY6UqQm2swUiBMTQQVFMOL/8GG47LL6j004N6ang1yomb1IxUGMLsWiyUPcs6dWFM4UiOPjcMkl9R+fsDjh+6htw+tfb6IwqtVgo2Z8vDFja0ZkPzIC9Pf3s3v3bnbv3s2nP/1pPvzhD/vPrw1n2AK2bfO+972P73znOw0arSAIUWR0NBCCnkDs7AzeF4EYXWYKxEceqe1Z6ThGNArRJ1zBVKlagSHXX3QpFmFw0LhQ3jlLpWDLluAYyWGLLqVSbVugWAw2bQry8L33RCAuHRGIDaKrq4upJcabaK05ePCg//jBBx/kiiuuWMnhCYLQZIQnvljMhJh6AlFrCTGNMmGBGI/DQw/NHdomRJ9wD8SZIcFy/UWXQgFeecU89lzf97zHiAwPz0EUosfkZLBBCmbus20TdaFUIBBlo23piEBsEP39/dx0001cc801fPzjH1/wWK01v/u7v8u1117Ltddey+nTp/nLv/zLOo1UEIRmYHw8mBxjMVOi3a1tBYiDGGXCRWqOHTP5hzMFoixsmgPPQYSgmjBIFeGoMzwc3B8ty2zU/PZvm3+LRSMwRCBGl0wmeBxu8bR1q3nuOOYrfJywMJKD2EDuvffeWa/dfvvts16zLIsnn3yyDiMSBKEZcRyzMA0LxJ4ek1MTPkYWqNEk7CB6t/q5cp+E6JPNBiGmtl17HguF2nwoITqEry/LMq1mjh0z91CtzXmrVsXJjyqe8POcey96pqfHFKzxXP1czmzIJRKNG2uzIA6iIAhCkzM9XRvWZtvGPeztNe9LkZpo4zVVz2Tg6aeNIFSq1n3yihAJ0WZqau4QU+lFGm3GxgDtoKemqIxNUMhrJiaCHDaQUO8o4zuDpSJqfIz2Qy+B1igFHR3mLe8aFBdxaYhAFARBaHKmn99Pce9B9FQWyzKTooSYNg/FohEWL7xgcmkyGfNVLpsvrYPeekK0mZmDONMJlkqm0WRiAhhOUx6bpDR0lvZXfs769XDddeZ9L49bnPxoMjUFulKB8XFUPkvXv/8rfOlLQG2xNgnXXzqrWiDqcHOUVchq//yC0BJMT5N91/spnhpBT05gTYxjWSa0ZubutwjEaFIqma+hoSBvzdvtrlbFQWwmJieDht2WZcK9PaTVRXTJDE2hCzkcLCwctux/iCuqr3DRRcEx4iBGl8lJXHteo3BoJwf/8A9AsFEqDuK5sWoFYiqVYnR0dNWKJK01o6OjpFKpRg9FEITz4cknmc46FEmhUdjTGazMGD09s0NMJbwtmhSLxiHMZk1xhd5eI/DD4kIWps3B6KgREl4Oopdv6F2DIhCjyeSJDBoT021TZQOn4W/+xheInoM4MVHbeF2IBtksJtwCsDyBuHcv7N1LT09wnBSqWTqrtkjNpk2bGBoaIp1ON3ooDSOVSrEpXMNZEITm4+xZsnRSIIlGoahiHdhPt74KFZoZZXEaXYpFOHLEPPZCEm+8EU6cMI+1DqrUenmJQvQIhwKH84E9JMQ0ukyemQbMhrlNlW4m4Zvfpv///kts+yq/SE25bM5hV1djxyvUks2CLpuqbBaaDtxwmW99i97evwICkS8CcWmsWoEYj8e59NJLGz0MQRCE8yOdJksHRZKAwsbBKhXo/us/wf78l/1wRZDFaRSpVEzVy5MnzXNPIL73vfDgg8b11dqIyFwuKLggRI98Pgjj9ooMSYhpczA1UkK7AtFC00MGtMb6fz5De/u9eG2rvTBTEYjRYmoKKJUBGwuHNtxeM9/8Jr3v/iv/OAkxXTqrNsRUEAShJUinGaWfqns7j1EhRYHEPV+h8/EHa4pk5HJmgSNEh2IRjh417oRX1GTdOnjjG2uLK3guohBdvB6IntOrVCD4JcQ02mQzFXBDTC2qRiAC3HcfHbZJKvUcKAn3jh658QJaVwFQOIGDuHcvfeUzQG2YsLA4IhAFQRCamXSaEQb8/JkYFRMeBcT+8EPELDNpSh5iNCmVgrwmT1D8yq/gFxoCWdg0C+EKpl6xoZlVTKVQVDSZzmq81EI/xBRAazpHj/rHSSXTaJIdCmxBC21yEF36B5/2H0uI6dIRgSgIgtDMpNOM0VcjEL3dbzV8luTEWf9QxxGBGDWKRfMFgeu0YYN5PrO4gixMo43nIIIRiOGG3eIgRphSieliLFSkxgkcRKB75BBUKn5VYXEQo8f02SkCB9ipEYjrXvhXQKqYnisiEAVBEJqZ4WEm6A0WN53tNYubZHbE2FRIL8QoUiwGbRG8giaeqOjrM/+Kg9gczNUDMdwmQWu3HL8QKZwDh8jT5j+3YjF6rtjoP+8hA9NG2ctGTTTJj+Z8B7gmxBS44MSzUDEVTuU+unREIAqCIDQz6TQZevB2T2OXXkT3+qCSSZIilIq+yBAHMVr4DmK1ipXPQrHoF8DwBCLIwrQZCOcgWpZxgy+5pLbyrAjE6JF98RBlYoBC4WB3JOj4qz/131/DGBTy6EpZrsOIMj1e8h/bOLTFKv7zdaT9XbhqVRzEpSICURAEoYmpDI8xTWcQYtoWp/vWG/z328i71d3EQYwipRIUs0UYSaPSZ2DXLjrHjgMwMGCO8cS9LEyjzVwO4sUX1xaqEYEYPUZfHAJAYxbFPT0K9VvvhSuvBKAP98LLTkuRmohSmCr7c6DCoeO9/9F/b21IIGotvSyXighEQRCEZqVYJD9Vpuj2QLRwsJJxurcG4VFtFPzwGilSEz2KRSgNZwCNTRXKJbq++LcAXHBBcJyERkWfmTmISsGmTbUOotcuQYgO6b3DAG4fWYee/rg5gR//OAADmH7ZulQWgRhFSiX/ugPjILb/nx/2Y/bbKJKo5sF1gKtVyQVeCiIQBUEQmpWREfK0UXAFoqmaqOjZuh6SSQDayIF20JWqNOqOIMUilApmO9tys2g6v/81GBxk3TpzjDiIzcF8DqInELUWgRhFRg8HMYcKTe8GNx/xjW8EYB0j5nmlItdhFDlwgAIpvDQLFbdpv3wT3Hqrf0iKnO8iSiXTpSECURAEoVlJp8nRTt5t8BxzXYue/hhccw3gCkSAclEcxAiSy0G57AnEKgpNh56Cv/1bNgZGMNWqLEyjjNbGWapUAoGYSJieluFWFxLiHT3GjpuT4jmIvZd0mzcuuwyU4gJOm/edKk7VYXxcQhSjhN67jwIpv0iNlUrS3g781m/5x7STh3xBKpmeAyIQBUGYRbkML78Mo6ONHomwIOk0edrI0w6AFTM7qN3dwHXXAdDpCkRdKksOYgSZGNfoiulVaVOlk6zZB7/nHi4oHfePE+ci2pRKgTvo9T/s74eOjlqBmM2KuIgU4+OMZWPuE4WFZs0lbn+ZVAo2b6aPcWwqgMLJl6hWxQmOEuU9+6li4zmIiY448TjwG7/hh5l2MA1OBcolcRCXiAhEQRBm8dBD8J3vwJe+FJTgFyKI6yAWXAfRa5PQ04MvENtxY0rd/BlxEKPF5OlpwHMQHTq981WtMvBPd/jiolo1BU4qlbl/jtBYwhVMvX6W/f0Qi2EWq+D30QvnSwkNZnCQcdYE7lMsxpr+0NJ461Y6mCZJCQ04RVMtUzZrokNu71EclH8OO3vdC66/3w8z9dpe6GJR8rmXiAhEQRBqyGTgX/4F7rsPHngA9u1r9IiEeXEdxAIm3zAWM7f0sIPYhbvVXRYHMYpMHhsLFqdhgQjE/tdXaIuX/eey8x1dpqaC/EOvQE1vr3mvLWixh9aSBxwp9u8nQ6//VCVsc//02LqVLqZIUkSjqBbNDo0UqokO0/uOo0NypnMgGbx5883mtdBGqYSYLg0RiIIg1PDgg7Brl1nEnDplnEQhorgCsegKRDsRCjHdtg2U8idGXa2gq1VxECPG5IlJvNAom2og6AFKJdqm3QqKUqgm0uRPjpF/7Ck4ewZVyGFZQR/LVCo4TgRixBgcZIIev0WCnYyZCAyPrVvpJEuCEqBw3JZBIhAjguOQO3gKB4V3H+3cEFL4O3YA0INRhLpcFgdxiYhAFATBp1KBf/s3EwZVqZivw4cbPSphXoaHydBDBZNDE4tbxGKuY9HRAVu30u0LDoWTK4iDGDEmz0z7DqJNlc722gS1ztHjUK36AlEWNtGk9Hf/QOHYGXSpiJU+gzU6zJo15r12kyLsn0MRiBFi/34mCQSFlYzPEogd5EhSBMApVtBaNmoiw/HjZIs22gsxVYrO/tCOzAyBSLmMdrQ4iEtABKIgCD6vvAKnTxuBCFKWPfKk04zSh+9AJWx6ekJ913bsoAvTmVsDOi8CMWpMj+Txzp+FQ9ct18OFF/rvd5OBnDlp4iBGl+Lzr5An5VbC1Kijh+nd9Shg9mo8xEGMGK5A9B3EVKJWIF5+ORaaDi8So1zBcbQ4iFFh3z4yeCdMoWI2Xd2hxqMbN8LAAD0EO2tOviACcQmIQBQEwefZZ2F4uLbK3uRk48YjLEI6zTh9ofCoGfkz110X7JwCTr4oIaYRIzteCc4fVbou6YNPfMJ/3wjEHFo74iBGmNL4tFtq3whEC4e+u+6AO+/0BaJXYl82aSKC48CBA2TpxO+hl0rU3kMvvBA6Ouj2N9o0TrEkGzVRYd8+pujyozBULFazIYNScN119GEUvQacXF7uo0tABKIgCIDJNzx2rLa1hbfbLWXZI0o6zTi9vsCIp2KzBOIavJWMQheLsjiNGNlM1X9s49B5cR/8/u+bBnpALxlAQ6EkDmJUKZcpTRXI0wZuLz2FNtfeRz9K57E9/qESYhohTpyAQoEsna7AUNiJGSGmSsHll9ODt1OqcKYL4iBGhVdfrQ0Rjtu1AhFgx47aeTAnDuJSEIEoCAIAP/85jIyYvEMVitAoFs2XEEHSacZwE53Q2Mm4XzkRgOuucwWGG2JaLJPLOvUepTAPulwhm3edCzeLpnPzgEla+43fAKAPs2PjlWcXgRhBRkYokaBAW42D2OuGtXU+86jf20JCTCPE4CBlYhRJAQpiMWIxZguMrVt9gaEBZ7og12FU2LevJkRYJWJ+zq/Pjh2sxS32BTi5ogjEJSACURAEcjnYswdOnjTPwy6U40iYaSQpl9Hj44y7AlGhZxdYGBhgzXovYd/4GrmRaXGEI0Ll6BAFTA8EhcaKx+la5/ZEcMuz97sCkaI4iJFleJgiSTfEFCylsMAPa+tiCrJZP8RUBGJEcPMP/R56MRO3MOggAAAgAElEQVSBEd4gBWDr1lCIosIpFMVBjAr79rkhwgYrEZ9TIJr7qAb3/OVyUC4jLIAIREEQeOEFc7M8e9Y0de7sDCZJr0G3EDFGRymRYBqz3W0psGxV6yAC7du2YFM1DiIWZLOShxgRSoNHKZAATIEa2lJ0emudt74VINj5rpRxqlXJnYki6TR5UhRJGAcxmcTadjXdyijBbibBqYBj8kglzDsi7N9Phh4cLECBHZt1/wRqBCKYXO7xcUm9aDjpNIyOhgSiwprLQbz8ctrjDgnKZh6sOlAqyrpmEUQgCsIqx3FMcZpczhTA6OyEZLJWIEol0wji9kDMuw6UbZuXaxxEoO36K4lhmjtrFExlW3aB6pWfb5aFW/HgCb+HpYUDqTa6utw3L7oILr2Uda5ABHCyeXEQo8jwMOOs8fPYVMxizev6sK7cCoRK7FfKLe8gZrPw9a/Dt7/dBA7N4KAvEI2DaPutSWrYupVeJrAxBaWcfFEia6LAvn0Abg6pAttGKTVbIMZidFxxEQnKeJE0ZLOy2bYIdRGISqmUUurnSqkXlVKvKKX+2n39CaXUbvfrlFLqfvf1W5RSmdB7fxn6We9QSu1XSh1USn2yHuMXhFbm0CEjDCcmzM52TZsEjICUeP0Ikk6To90PUbTdu/lMgZi6/hpXICqqKPTUVMs6iN/9Lnz+8+bfZqB4aIiSLxCr0JYKBCLAzTdzAWcAN7TNFYjNIoBXDcPDZELFouyERV8fsH07UNuku9UF4vPPw/79JmXhxRcbPZpF2L+fCXpMZIWbg9jfP8dxW7bQHuqFWC1VQTuyWdNoXIHoRdEQi2FZc+SQAslrLyfhnT83kkbWNQtTLwexCPyS1no7sAN4h1LqTVrrt2qtd2itdwBPAeFp/QnvPa31pwGUUjbw/wL/EbgK+B2l1FV1+gyC0JKcOAGlEoyNGecwlQLLEgcx8sx0EGPmhM0MkbKuv44EJf+5zk4zPdV6hWqqVbMoBXj5ZVNsKeoUj5yiFA4xTbUFIaYAv/iL9DOGjal0Ws3mKZf9eidCVBgeZpxe/H6kMdtch9u2AfjFaihXWr7NRdhVC1fEjhz5PBw/zij9bvg9qPkcxK4uOvpSJCmZjRoU5POSh9hoZjqIsTiWBW1tsw9Nbr/CF4gaWxzEJVAXgagN3p5Z3P3y90CVUl3ALwH3L/KjbgQOaq0Pa61LwH3Au1dgyMJ5UCrBQw/Bww83QYiJQC5nJvXJSSMOlTIC0UMcxIgyPEyeNgqYIjR23Jy0WTk0l1xC0grUknYccoND9Rpl3SgUap21ZliEF4+d9gWiPTMHEeDmm2mjEDgXhaI4F1EknWaKbn9RYycsIzRcB9EvcFIut3ybi1KwFxXtEMyDB0FrRjGWobZiKMuaWyACnZcMkKSECVG0IJeT67DRvPoqgL9Jim3P7yC+4WpS7kZp1U21kHXNwtQtB1EpZSuldgPDwKNa62dCb/8G8GOtdfh28mY3JPVflVJXu69dCJwIHTPkviZEiF27zNezz8Lu3Y0ejbAY09PmK5MxAhGMQPRE4mppzp3JmE2N555r9EiWiBti6uWweQ7izBBTlCLZEQPcMEUspl86VM+R1oWZrViaYRE+cXwyKM+OQ6onRSwWOuB1ryO1fo0vEB0UTE2tiuuxqRgeZpIu/1xaCduEmLoOol/gpFJBa90Uf5uvlbBAjHTkyf79AIzRZ57HYiiFOW9z0PG6dSQomjYJWJATB7Hh1ISYKoibczgrBxFIXHc1SQqAe/7yOTLD0r9rIeomELXWVTeUdBNwo1LqmtDbvwN8PfT8eeASNyT1HwicxZnFhyHkRIZRSv2BUmqXUmpXOp2e6xBhhTh1Knh89mzjxiEsjclJ47xMTZkKph5hF3E1TIQ7d5pNjQcfNMXRIk86TY42iq4DFUuYEzZLIAKp7oT/WKOY3ne8LkOsJ4VC7fPIL8InJxnPqMB1wqFzIFV7jFKk/sMN/sKmig0TEy17PWoNjz0G997bJNegx/BwTS82O+lWw9y4Efr7aypg6lKZbLZ180ibxkEcHARgwisuZJv8tTmrmAKdWy90Q/UDB7FVr8OmwA0RBuMgmnNoHMS5BGK8r4tUm5kjvaJEE/tO1224zUjdq5hqrSeAncA7AJRS/ZjQ0YdDx0x6Iala6x8AcaXUAMYxvCj04zYBITlS8/+5S2t9g9b6hrVr167ERxHm4fBhePJJ83XsWKNHIyyGt1iZmgoqYULwWOvVIRBHRoLHTbE4TacZYcAtsAB2wqa9vVbke7T1eUkZZnGTO9CaIaZhIi8QjxwhQw9+3lrcpqt79h5o8pY30+YKRI2FHs+0rIN4+jT89Kdm7f74440ezdJxzqbJ0RESiHETqqgUbNtGF1kUJu+3mi9RqdQKqVZipoMYWSHsOogmdxS/wMlcG2wAnVdeTDLsIOYlxLShhJyIPG1gxUBZ84aYKgUda4KNUgeLzMFmmOgbR72qmK5VSvW6j9uAXwZedd/+LeAhrXUhdPx6pUyJDKXUje44R4FngS1KqUuVUgng/cAD9fgMwtJwHPjOd+DAAfP1rW81ekTCYkxOmhBTx6l1DcOPW3VBGiYsMJqiymc6zVnWuU80djI27+KmbW2ne5TrIB4erssQ60mxaHKeR0ZMgZrIC8TDh5mg13cQVSJWW8HUxbrlZjoxH8ZB4WQyjI+2XpEhqM11bqbok1I6Q54UoMwWTCoehCpu306Mqlti3ziIrVzJNCwQq9UI30sPHABwr0G1qEDs2v46N9RbGSdfchAbiysQNa6D6O5oz1ekBqDrgjb3e8xGaea4JCEuRL0cxA3A40qplzAi71Gt9UPue++nNrwU4L3AHqXUi8AXgPe7hW4qwB8BjwD7gG9qrV+pyycQlsTp02Zir1TM17FjrTsRtgJewQQvFMhzDfv7ax3ESIcKLRPhHLbILmrCpNOkXYGo0NgJe97wqPZ1XXhOlUaRmyzDmTN1Gmh9KBTM37L39xz5+87hw0yF8tbsVKy2QI3HlVfSFQ9W3Y4DE3vnDJxpesJFzSYmzGeNPPk8pWyRAik0ZlFlxUObNdu2ocDPI9Wl0qoRiBDhuePoUQDXxccPT5x3k23rxaSU+XAOFrpcZuy05LA1DFcglkjgYINlFizJJLV53CG6L+z2H2sUmTPNMNE3jnl+jcuL1vol4Lp53rtljte+CHxxnuN/APxgOccnLB/79hlB4ZWYt21Tev5Nb2rsuIS5KRTMLu/kpLmpeq0trr8e9u41j7VeHQ5iMwrEEbcCn0JjLeAgtndaYNvoqlukhg6zg75+fR0HvLIUCoGgKJebYAF+5AiTrMUPMU0m5haIlkXP+hScCHa+x18ewmRYtBZhgejdl+bb9IgM6TRFkpRIGLFvgVKKbm8t6lYy9QQiJVPJtBmq7L4WSiUzZ3hzydQUbNjQ2DHNolg0u9kQbNLYNrZNcN5mYCdsuroUTBonX6MYH8qCWyRMqDPu+ZumwxTvcgXiXPmHHt2v6wemAwdxtGpuNOHcGsGn7jmIQmvjVo720ToQGkL0mJ4OFmLhkNLrr699HulckmXAcZpMIFarMDrKOKYmuxGI8fkdxHb8bVWNMgLx4ME6DbY+FIvm1wLNE2Lquxdo7LbEnCGmAL2bvdr7rkA8MDL3gU3OTPepKUL4hocpkaBMAlBYlmXynbw8qKuuAtsm5eaR4lTQ5Ur0/z5fI8WiMXe8AIVIOohucZMS8SB/DYVtz52/5tEzEAiJKjbjZ0otPS9GGtdBzNGORvkhpgudvzUXdYEKCtVknE704IEVH2qzIgJRWFYOH54tEN1ccCGC5HJmMe05iB5XXx20vABavjm3tzAtl41YjPxnHRsDrX2BoVDYtjWvQOzqAmyzCNKeg9hiAnGmgxjpEvtQIxAVoNqSczuIQP/Vxun1ckgnhlqzDObMvrlNURwrnaZIwq8mrGxVKxBTKdi6lRTmpqJR6OnplhSIjmPupeVyEJ0SyevQrZ43SbcJF7XN5NfZGTifc9F1QScxKr4D5UznpZdeowgJRIegL9d891DznsKOW+YaRFEhRu6Zl+sx2qZEBKKwrAwNzRaIh1qv5VrLMD0N2aFxKukxrDNDMJ2lvd1UZ+/qCiZLz2VsVbwCJydPmiInkXcQh4dxUEzhxkPZ1sIV+DqBmO1W4FPkaG9Jgeg5iGDcp8hqKMeBI0fI4q1mNFYqOa+D2Hf1elwZaRzEYpvfA6yVKJXMfeb0afMrahYH0bS4MFi2mp0HtW2bX4kWwMnmWlIglkq1eaPe+YwcofxDB8s/WfNdfx5t67tJ+D1JLSlU00hcgTjtVg/2HMSFBGJ7OyRS5jjHlT8Tz4qDOB8iEIVl5fTp2QLx+PEIL9RWObkcjP30ZSgViU1PwCt72XKZRqnaXIxWF4iFQuBelEpNIBDTaQqkTHgUJoVCqQUq8PkOYmuHmIYXp/l8hFsJnDoFpRJTrkBUKKxEfN4F6sA6G+IJV+DbTNJNdecT9RtvnfByR4tFc/6aYvE9PMw4a/xiQ1bMmr1I3b6dNrybikJP51syB3GmQCwWo+8g6pCDON/906N9Ux9JSr6D2Kq9EL1128zWQZHCzUE0PRAtPwdxvhxSMGZ+vC2IpAHIvHh0hQfavIhAFJaNSsX0j5spEKemmiRUaBWSOzvFxIRbJIMq5Ka5vN30yOvpWV0OorewqVSaoIBEOk2OdgqYOOBYzJyo+UJMu7txE/HNxOg7iC20czPTQYx0oZrDhwHI4cYhxmIoNf/ud3c3xNvM+fN3vn/8XB0GWl/CAqNYbJJ5I51mkmBVOqdA3LaNDsxNRQM6Jw5iQ3EFonEQlb95tphA7Ni81m91YXoh5hk+XV34m5qQRx+Fr34V/uf/DAoORo6Qg1hF+SGmC7nAbW2Q6Er4kTTQuhWhlwMRiMKykU6bhfVMgVipwJEjjRuXMD/TB04x4eZBxTAzwZbhJ4Fageg4EZ3ol4lwgRMwDmI1yvN+Ok2eNopuBT13fTPvAscIRMvd+XYdxMlJE0/bIsx0fSMtEN0bYg5Tck/FTIn9+QRiRwckOxOBc0FrhkZ5OcBgBH+kw4Q9/BDTwEGcVShj+3ZfIAI4uQLZqWbo4XFuNI1AnBFi6oUnrlmzwPcA7Ws7abfLrsCwQDuc2tMMuxjnxuCg+TeTqelHHx1CvbmmaXcdRHNfXMhBbGuDRGcSsPz7aGa82nItn5YLEYjCsrF/v5kctIaEydf3BWKLRbO1DFOHzviFMmIYRXT5K98DasVGqzuI4QInYBaqkQ4zTafJ0hEIxLhZ4MznIJpzqSAWR6PcsBxa6sLM5cyC5sgR87caaYF4+LDf4BkAVyDOt/vd3g6JbnNTrbrT9vjJ3OyqLk1OuRwIwlLJhJlGOswNfIHoYcXs2UJ/40Y6k+b+qlForZk+0Qzxs+fGTIHoRWNE7s+0xkG0/DYHfX0Lf1tHB7R1BLnAAKf2tl4PqHBF70iGebvhpQBZOtFuFVqlluAgJhU6Fg9CTOmBl6VQzVyIQBSWjfBaMx4PWss4TkvWU2gJRg9NUCLhBh462FTZtOt+mJqir291OYgzFzZRF4gT9FJ2z50dN7fyhdpc2DZoO2Z2zF2R2EoC8cQJePpps1H1s5+ZhU2UBWKFmC/wrZi9YIhpKgXJlA3Kooq5sU44Xf5Ct1UoFGodw6YIMx0eDnrpAVZiDgdRKTrXBy9qLLJHW8e995gpEL3XIjV3lMummh5GHGgstJu/tnbtwt/a0QHt3TE/Fxjg1JHiwt/UhIRztyPZAzkkECfp9nNILcuIwPlobzdrU2JxE5aKKxBfemklR9u0iEAUlo3Dh4PJIZEIqrhpbXpyC9HjzDFva1djU6WfUexqCR5/vCbcZjUIxHBIaaUS8VYX6TSj9FHFMuI+bhGLzT85plLu9Riz/YVsls6WEYhaBymVXv/OoaGILm4ADh/2m6sDWAmz2Jxv9zuVcs9tLOY7F+OsaZnz51Eszn4eSQcjTDrNFMGJs+KxOYV+9yZv98aEeWdPRvWP87Uzn0CMVKGakyf9QWbowbECN2lJArHX9Lv0rsOTUQzBPA+0rr0OI3kPDcW9Zun0cywsa+E+iKmU274rZgchpiIQ50UEorBshFtczBSIR47MnjiExpMeNqrIcxAHcHe1f/hD+vtrHcRITfLLzMwQ02ZwEM+y3n2iseOxmpzRmaRSkEwCVsxfDB3n4pYRGJVKbSiiUia0bXS0cWNakMOHmaTLDxe1EzFiMfcczYF//mw7CDFtQYE4M5w08g6i1jA8bMLcPAcxac+5SO28dMB8CybMNHsm6pWwzp2wQPT+lovFiG0uuvmHABP04MTi/vOBgYW/tbMTUr1tKLR/HU5ldHQjFV4D3jk8e9bMgZHcoAkJxCm60DGzwaaUcQnnI5Fw05/icd8BnqBXQkznQQSisGycOhUIxHi8Ng8xn49osvMqRjua0XH3xgq+gwjAD39I3xpdIxAjOVEsE3M5iFEXiMOsA1xxn7AXrMDX1mbcKR2L+dXbDrClZQRGoRCERSm3oN30dET/ZnM5OHOGMfp8UaESsQWbdPsOoh04iBP0tlyTWc+58Fxgr1BNZMlmoVAI9bNUWPYcOYhA7xXr/ccai+xoa4YmegIxlQpei9TmYigsO0MPjm0EolKwbt3C39rZCaq7izby/nVIId9Saxsv9zefNzndUXcQp+jyW1xY1sICMZn0HMRgHszQA6+8EuFyrY1DBKKwLOTzZqc37CB64VJSyTSalI6fYbJqYhItNzNtLWnz5pEj9GSO+3mkWkd0olgmmjEHcQSz3W0EYmze/EMwk2JPDzUhpoNc3lIC0SuEoVTgIEbSfXIdjAncGG7LxrbnaI0Qwg+Nsm2qmNCMVnQQPYEYj5vFnudkRJbhYSBo1o1lYdtqznPZc/Um95EbYppTEbPWzp+wQOzoMNdhpRKx6zAkEMdZ44c6WdbibS66usx/agRiPs+pk1Evtbt0wpulxaKZ9yMX/RXKQZymwy94sRQHMZUCLJuqZRyMDD3mD9cr3Sr4iEAUloWREbNLqLW539o2XHedec8TiG7rLyEi5PYcJu+W2bfdCqa+gwh0P7/T38mH1haIM3voVasRLnDiODAywhim5J5CYyfjCwrEZNIt/23ZfmjNAbaYlVukVm+vjWIxEIi2bRYKWkc0asEViOP0GlFh29j2wtX3kkkvB9F2t3Ja30H0HKjQWjB6pM2GmmlXokBZ87Yr6R5IuiHeJsS0SJLKC60V2hYWiAMDQRSRq6OjQSjEdJw16JBAXKhFAhjxoVJJ2qyiH2KK43Byb2aFBlt/Zm6WFgrGSYwUoRv7NB1+kaElO4iATpjN8QnciVPyEGchAlFYFo4dC0qUe8Uy3vIW8544iNFkeu8xvw+b7fZA9HMQge4nf7BqHESvII1SQe5s5CZFj/FxqFb9/pUKhRWzFtz9jseDVhdOzKzaTnIh07S3hMgIO4jxeBCqefp0BPvonTgBGIGIKxAXanEB5m/S9LK0cVBoFEe4lMKhkxHc3n9taB2ECVtWkMM2MhLh6C9X+eRpN21jLGveQhltbRBPmD9Mz8Wf3tVa5b3DAnHdukAgRqrdqusgloibSs5ugRPbXrjACRhxYdmK9jaoYuPdWlqp1cXMQkOFQgTn/hkC0VuoLFakJpkM7ivVeML//hxtkoc4ByIQhWXhwIFgIRaLwaZNsGNHsJMvvRCjR27/CXNjxHUQB9bWOIhdT/wrtgpmisnJCC62lwkvnNSymkAguq7FpCcQ3UXpQg5iImGEU2cnvkAEOMhlLXFhFgqBiIjFas/hdNRqgRw/DuA7wNjzu05h+vsh1W7jBYQXSfJ06TpTlbEFqFQCFz8WgwsuMI8jXcnUFYi+g2hb8wqNVApiKZtwBczsi82/ORMmLC7Wrg0W45EqFuU6iJN0GzfevVm0tc2fA+wRj5uv9k7TKsgT+qcORjkf4dyY6SB6YaaRIiQQc7SjzyHE1LbNMTqW8AX+U7xZHMQ5EIEoLAuHD9cWqNmwAbZsqe2FeOxYbX8dobHkDp32Q0xjVGDtAAMXxPz3Y/kpUtWsP2lWKhEOuzxPPIFo24G4iFRhhTDpNBrIeqX13UXpQg6ibZuv7m7cJsGGVslDDIeYeiJfKfP36urpuuE4i1wnroPohTapJTiIYETH+vXm/HkCYye3tMT5A3P+whUw17s1XUqliDlQYVyBWCBV4yDOJfaTSYinghBToOV6IZZKgcgPO4iRyWNzHP/6Mz0Qle8gLrZB49HWBu3dccJC/9TJKHy45WFmwbbIVRKemqq5weboBOVWg7YXFoiWZf4mL7jAmwfNdfg4bxOBOAciEIVl4cSJUIGaWIWNB35C+913sqbH3Di1NguAFuvr3NRkj6QpYALyY1ShvZ2BX3lDzTGdhWABU622XE0Fn7kcxMh+1nSaPCnyJAGNFbNQavECC8mkK0JCvfRapZJp2EH0xHCjWl0cOQK7di2wqHIdxIzrAC/VQWxvd0VT6Pw9zZvI7zu6LONuNKVSbZGz/v5AYLh9zaOHu1mTJ4WXg7iwgxiDkPOUHYqaNXN+eG1KlDJ/z17rnVIpIhEZp0/7O0leBVOv7c9i4aUe7e3QtsZYo951mB6x/A2qZmemgxi5QlGhpGQN5BNd4FWDVvP3AvbwNp90LOHn4z/DLzB9fCSCVmljEYEonDdah3J9qlUSB/ay8Vv/Az76UTaMvOQfI3mIEaJaJX2iQNW9QVpUSfa20f6f3lZzWGfmpO8gVqsRdtXOA68NCzSPgzhBLyUSpoJpTKHUwiGmEOQhhndOW8VBjJJA9P5/8y6IQw4GgIrF/AX1QnR0mPOXSmp/YVokydNPtMbKNOwgplLQ1xcUlIhsFO3wMBViFDGCQcUWcRDbTUsFr8DJ9OnWitv37qOeU9PTE4j8SBQbChWomaQbJxGoiaU6iO3tYHd3kqLgX4c6n+fMmeUcaOMIhwnH3RaRkTh3HqHw0gIpnFjSv4SSySBqbT7a2kwkTUenwmkzuwIVYjzJTbBnz0qNuikRgSicN5mM+dLlMtZYmnhxio2Yi3jz8M9BOyIQF2BysgE5GseOcUoHffRiMYuB9XHU23+ZcOnS7uxJcMzKu1qNyC7wMlOtmsXp8DA8+aRxf7xeUOFQm8iQTpOhh7IrEO2YOV+LCUS/9UzIgTrOxRQOnFjhAa88udzcArFYpK4Lt3I5cKPnzH10HN8Om/JChGNLCzFtbzefaf1ANSixD+x8cc0yjLzxzGyyvmZNkMMW2cX38DDTtFPxNtpsNa+LEY9DvM3kIPoOYsGOWPze+eE5iJ5A7OoKBGIkXKiZPRATgbhYLALDo70d6O6mg+lQq4tCNCsmvwbCDqIXrhmJc+cxI//QiQc59Yu5h2A2nZSCCy8E3R7Yxo/xSxJmOgMRiMJ5MzoKUyMFdHqEmFPCwmEDZstpC/uhUpFWF3NQrZrWO88/bwpoeZNrXRgc5Axe42aNlYrT34/Ztr/xRv+wHiZRRTMwx4mwq3YeFApGDL7wglmrHT9uTLXI9kJMp5mk23UQwXIF4lJCTONx6OyJ+WFVGsWhdFeE42mXRjZr/j69HoieQIT63nPCv8Y5BeLwsJ+IbXJIFcptjbCUHESACzZaNQLx6aELfeemmQk7iIlErUAcHo6o0TY8zARrfMFnxUw/y7mKnZgCJ6qm1UyWzpbKuygUzNfevXDvvYFQhIiIjJCDmKEHHU/5z5cqENvagI4O2sn7ETiUS5w82AIXIbU5iJ5AHBuLUCXhkJ1pCtQk/HvDUsKEvaiEDRvAaQts413cwNSu/cs50qZHBKJw3pzduY/c4bNorbGpYFNlPWbL91pehkrVz0EUB9EwNgbPPlvbp62u1RYHBxnGlAlUaOy2BGvXuu/96q/6h/UwAQXTnKxVcxC9KolaB+7T2FiEeyGm04yxhjJxE2IaN4uUxXp4eQu13jUKnQwWRgfY0vStLsKpI5ZlzqFnhLspf3Uh7LAXCnMU5jgRuLXZGQ2elxJiqhR0r++gm+B/VMpVePqpKKqnc6NcNtp5zx54+GEj7L2euplMdDdrvHYzAMq25l2kJhLuNei2KgG3RH9ItDQ7hYLZaDtyBO6/Hx54oLZdScOZ6SDGA3GxZolGfEcHYFm0p5yajZpTe1rDCQ63fEokzL+VytKKfT38MPzn/wx/8icraIzP7IEYi/vPlyIQPZexsxMu2BjEo1ax+fenY/N81+pEBKJwfgwOcvCjn8PRDhpFjArrGCaByYvZwW5Utew7iOl0a7pQS6Vahf37TSRDoWBcgz632n1dXYDBwVCjdbDaU8ZBBLjlFv+wXiZQFXMuHad1BWK4/5ptm3OjdUTL6w8Pk8ZT8xo7btHVtXjuhbdz2tsLTiqIxWmFPETv71Kp4Bx6DA/Xb/c7kzFCJ5021/osURNSqznawbL9MS/mICaT5jjVluIq+0DwRrXK4w9HUT2dG6US7NtnInCPH4fPfMacz2TS/C4jl4foOJBOm2brruCz4/a8Qj8WCwSiaZHg5qG2kIOYzQabJJZl2l/l8+Y8jo9HwIWa4SCGwxO9eXgxPBHS3mnXCsQDUdxNPHfCBdu86w8W32gbGoK//3tj8D3/PPy3/7ZCAwwJxDxtOLFA1C0lj9SbB7WG6/9DbUzqYwcvjki53WggAlE4P/7mbzhc3OhPkHHKbOwPelmsY4RObRRhpWKuvdXsIh4/bm6glgWvex284Q3BxFTXHfIDB4I+bGjs9hQDA+7Tyy7zD1vDuC8QW9VBLBRmC0THMa9FsqhZOl3j/loJe0nhUZ6D2NMDOhnUAm8FgRh27rxz6IWbjo3V59pyHDOOhx6CH/8Ynnpqjv9vyEHM0w52IBAXW9ykUq4rqhRb1tYmAz/z7+WmDzMtlwPXwbLMdZnJBAvUE1FLlZ2YgErF7TNXqS4AACAASURBVEeqALWgg6iUuwngC0TFES5tGYGodRBxEQ71PnrUhNfmchGIyJjpIMYCB9HfIF0E7/y29cRrBeKx1igWFS7Y1tUVCKrFciwffbRWWz3zzAqt9WY6iHb8nEJMvbBZx4Htb+3225wAPF++homXo3ajaRwiEIXz42c/4wQX+QIxsbaXjb9zi/+2hWZd1cSMS6GaQGBdeSVcfHFtY9d6CsTK/kN+kQwLjdXRFkyQGzf6aqKPMcABx2lpBzHcQ8/bOc3loisQPQfRhJjGFi1QA8FC2wjEIMT0KJsp7W/ui9KLSpjpIHrORT2urampQIwmk2YtOitsPLQNn6fNH+g5CUSgZ0MbFxAkdZWmCjz11HJ8isYR7qHnfc6xsWCBGqlKiuD3QMzQ7fZAtBc9j+vW4Z9zjeIAW9BHW0Mglkq191GljGt65IiZTnK5BkcPaV0jEM9yAdoOwhP9FItF8M5vqidVKxDP2NHMkz1HwpVoL7ggmDcWuv60hscew1zAI2nImhP9rW+twABn5CA6oXO4WBQGBCGmjgMDaxWbB4I/SgeLf/9W1G40jUMEovDayWYpHz7OGS4IHMSL17Nx+9qaLP0Li4ekkqmLt2D0bmT5PLz6qknqr9vkWSiQP2aq74FpcaHa2wIH0bLgkksA6GfUBENVK6tCIHq5a0qZcxO5qq2VCgwPM4ZJmFFo7GRsSQ6iN9HHYtA7ENz6q9gceaW5QxTDzkRY5FuWua7qcR4nJ81XKmUck1Jpjjwc1warYlEifs4Ooid88/2buIWdwZv5PDt3zvVdzUO5PFsgnj0b5EHVM1R4SbhJWb6DaCksa2EXo7cX4kkLr8l6njZOHKhndbKVY6ZABPP3OjFh7qXT0w0WiMPDfiW4IgmG7M04KhB1ocCZBfGu09iaTj+VBqA8XYpGnuV5Mp9A9MLm5+LgQRg6VqX8/Euk95whs2sQBgd59BFnedM0tJ5dxdQKHMClzIOeQPTaW73tmtqT9tjjyzLSlkAEovDaeeUVxlnDFN1oFLatiMVtNl6ahEsv9Q+7mGM1lUwjW7J8hSmVzJdSJuzmG9+A//7f4ZFHTMGaZ5+tU1uFQ4eYpp0C5k4Zs01lPV8ggol/xQhEAKrVlu2DGA4xDbdIyOUiKIiHhshX40zSC2iUZWPF7HNyELWGTZsTNe8dOLxIAmOE0TrYePHEVriSabVan0qmmYy5PpLJBSo3ug7iFJ1obF/xdXXNXfkyTNhBzHVvmCUQn3kmooVclki4rYz3OUdGzLWZSJjfbaSux1kO4vw9ED0SCeheY/IPvU3V/ceXUJu/CfDmNwjOn3ctnjq19EInK0bIPTzOxehUimrVnIO2tqWHmHrnt9rRTRehP8hCviVaXcwUiF4l2qmp+ef/xx4DDh9ifDpBlk7G6KN4Kk3lud3c//8t40U7NVUTljEd661xcZcSYurlcoOZ+9/2y7Vz3+4DndGsPdAARCC2IBMT/ty1srz0Ehl63MWOwk6YEJsNG4Crr/YPu4gTfiXTSqWl2j6dE9PTpkDNd78L991nCjJUq0EozrFjdVrgDQ4yTbsJcQPshLlB1kyQrsBfSxoLDdUqjhPRkMvzZGYPvbCDGKkFKcDRo2ToIUsnFg7EYljW4j0QIQjVcxzYeHntTDo41l/nMrrLR3hhGm5vEX584MDc37ucZDLm78VrJwJmQVxT88B1EMfowwGwLT/XZzHCAjHftY6t7PerRZPPUyrR1GGm4fL63nmLxczvMJmMrkDM0olxEG1se2GBmExCd1+ccC/EweyGlth5CzuI3vkLz21euHfDcmVDBWoO8zpIpfxrs7s72EBbDO9a1R1dtBO6ZxYKnDoRxca554bXbsu2jUAE87sJFyAKozU89vWz6JMn/TUFQIkEZDJ8/8+eovjU88szuBkKPN+7Hscxf2xKLV7JG4zY9TfacnDRL76O1xNU8dbZaX7yk+UZbrMjArHFmJyEF180IYvF4gr/z15+mSk6maKrRiBu3EiNQNzMUaiUcRyzEG9FkbEUxsZM4vZcRbLicXPzrcsO6+AgZ9jgl1qPJU3lvZoJ0hWI3UyhcPyVWyueO08ghvPXPIEYuXXbkSNM0EuWdl8g2vbSQmvC1dsu2BgLXsBtddGkTUoLhdmhbVArEI8cWdk+ermcGUMuZ65lb2E8ORlaEJdKfviEKRCl/CqmSxWIXohprq0fBYGL6P5PXnllOT9VfSkUgg0z7zzGYkaHJZPmdxmpkG9XIJr5D99BXMjFSCahp0ehrZjvfAxyeUsUqimVajfaPGzbXBfeV8POYeh3fD4CsaPD/ftMJEjFQ29ozck9zW09aR0IRMsym8a2be49hcLcrUr2/jzL8NOHKZHAQRGjSoIyZcwvJ5NP8OgvfsY0xjxfZgjE6a4NNff1pdxHww5iPg9ccw1vIxRXms/x9L+3RsGh80UEYgtRqRhh6F0wK+5Gvfwyp9lAhRgahZVK0NHhLlZDAvH1HK4JMfV6zq02Tp404jAeNzept74V/uiPjBbzHIdXX63DQA4c4BQbwCvNnorXhpeCLxC7mMIOCcRMpk5hsHVkerrWuQhPHuH3IsGRI2ToIUdHjYN4LjmIjuPuDLcFu72HeD2V/c3ZC3FmFVqPcCXT06dNq8eV2jTznK1yaF0Rj5tdd/8+fPKkf+MboR+UsaoXc508akJM490Qj7OFA8H/uFJp6hyofD6YF8IucDptPnupZB5HZu5wd/Om6SCcg7hYiGlPDxCL+Q5iqxSqmctBBCPyweyNNFQgznAQdTIQiL29wRy8GO3twWdKdCX8jVaA0/ujtINx7oTzgJNJ8zl7eoK549Sp2dffY//HA1As+Ckrmxji8p6zvkAE+Fb53ejb/jf46U/Pb4AzKuVMt6+t2XBfioM4M8SUri7edFGt8Dz2ctR2hhuDCMQW4tVXg90fWGGBqDW89BLHuMTsnqKw2+JceKE7OYQE4iZOYFfNysxdxzR1rsxrxbu3JRKweTPceisMDJh0P29yqku3gcFBTrLRfaKx2xOzBaKbg2ihaSOPqgatLhpeqnyZmc9BLBbNorUQoRoSxUND5GijQJspHnQOIaZhgZhMQv+aYGYtE+f4rnrEpS8/xeJs52L9+uCxZRmzZ2jIOPj797/2MLejR+fuB5bJmDGEi6h4eTt+5G6oT8MIA+dUoAbMYs1bmJYrFpXNlzFASBHm843N8TpPZuaReo+9qIVYzPyeIxMJHQoxDVcxXcxBTKUgEde+QMzTxokXmljZu4QF4kwHEcz8l802MAol5CAe4VKcZMoXOwMDi+cAeygV6oXYE6dK8GFPHV7psK2VpVgMIpzCfXNjsaDgUHjtVn3gYR5/2hxYwEwwl17dwZUfvIlqb1AW9jgX83PeCJ/4xPnt8MxwELOpAf/HLXWjLRxi6s0Dm7b3u51JDWePF1c+Aq8JEIHYIgwNGfs/FoMLLzSvragIO30axsY4xYXuRKeItSVMeCnAFVf4V2EPU6SqWdCO3wtxtSUBax0UrEgkavP9wgLRD4U7eHDlfkmDg5xhPeBWwWxLzk7QDxUZ6mAaVa0CuiV7IeZytcUxwi7UxES0BGLmYJoycSrEUICVWLqD6LVT8cKItmyuLQk5+FKEPug5MFeI6ZYtwcI0HjcLn7Vrzb3n9Gn4+c/PvZryqVNGIB4+PLv4TDj/0GOWgxhSlqP0+wNcaoipUrUl2guXXlkrEAt5RkfP7TNFiXA4d3ixrhSMjgZhppG5/7gCMUc7oPx80sVyEJWCns5qTXGNwZebfzUaDjGd6eSDuQYPHTKL8lJp9vcvN6dPz3ArXYE4RSej9OMkjLBRitkbpIvgneOOvlSNQDzZ5EVqSqW5BaL3fGrKGBGlEjA2xosf/ALjrEEDBVLE44pNv3YdHV0Wm96wDr31Cv9nf5PfNjt0Dz302gc4QyBOJfr8x+H740LMchCB5HVXsY5gg1Rns5w8+dqH2SqIQGwBJifNjReMLltjKuCvbDL4Sy8BcIoNRiDGYsTjKhCIbW2+C6WAbiZnhZmuJrymz14RlL7gvsaGDWbCqVbhyMEq//QL/8RnttzNfx34LPd9cjdPP20mu2UJrcpk4OxZ0qwDQKGw2uZwENes8eM1OskCDlSdlhSI+Xytg+gVqVHKfNbIOBZA5tgERZJUMFaSlTD/LsVB9BY1jmP+Hi+/ujam6sCh5pwOvPw/CBajYYEIRuhffjnceKNbRAuj15a6Szw9XevuHzgQLC683MPp6aB6KQQC0Rc+IQfxNBt9x8myliYQIXAuHAcKF18eVBkGyBmBOFeOczPgzVfetecRzkPMZiN0/3Ht2hwdxntQS8tBBDcPMRSaOHioeasIe4Sd/LBA9FxvpWBw0DxeaRfxxRf5/9k78zi5yirvf++tvXqr3tLpTqeTTkhCEiBhRxYFBUTEfXABxWV0xm3U0RcRZ8YZR0d99X3H12UUHXdFHZwRVFQEFBBE1rCEJITsW3en1+ql9rr3vn+c+9ylurq7ujvBriTn82nodFdVV9Wt53nO7/x+53f45Cfh85+3CzSW5UhMdyN5iRUSBrGmpjL3S2+o9Rppsc3C7BgfNataYeM1ilJgS+WTjY2y142PwxNPQOZDN/KHwVMB7P7DAMs2NhKIyAVfuxaKLe0O+t7EGexkJfzjP859kyrtQQw2+GTplVxHL4PoFH9PO03MFJ0HTnPw4Nye4rEU1ZkRnAgnCgW377CzU9bi8zJ4ffNmAAZYhIUOwRChEC5ABJ/MtMEDEI9VN8zpQiU2KoFUjF0mA9/9LtxzD/z+9xYP3T3O1x89k9/zEu4wL+MbX0xx4w0m11wDV14JX/7yPHvibDvHIewnENAJBPTJAFHTfH2IGjhOpgsmQTtC4XUx9c7PU0Y1C8Z1N5cjeThHlgiFEoBYKYOoEu9UClad5b/Tc4creJAFGN4+UXXwJxL2UHI7DENaAONxWLNG2ETLcmXf4+Pw0EOS+PT1+deYaYrjsGmKdLWlRT4vzz4rj6FYCmWwoiIUkvsMDtrFHQ+DeJg2h3GqVGIK7t5uGJBduooIeeqwEWg2U9XqDG8hxgswQiEXID5fMy0rCptBTCkGsYIxFw5AbA7IuWnH9kMVfgAWcKhCG7jXb+NGf6Fm715hn47mNczlBIM89BD8+tfwpS8hi8JGbrtZAbqOGZQCWV2dz6+ronCcTOsTJPAkM5nqHnWhAOJzz0le8uEPu9cvGJS9r7YWMvsHePTm7fyBSwBhD2looHuDe4ao9hmvGum/eIOQCz/72dyeYEkP4jhu06Guu/vjdKFYfPCQKCtX+gFiLuet5x23cQIgVnns2ydVkPp6h7AjGpUFkM0exWry5s1YQD+tNoMYIBh0q/NACUAc9QHEak1i5hqplB8gNjVJ0vjpT8OPfmS/H2NjkM2Q9lhFk8/DYXE+zGbh1lvh7rvn8UTsEq64KIJmu2CWnQFlf6AkAbWO2VmIav6at/fJOwtxoRQzCrv2kyJOSjEWepBAUHf6mmaKWMw9GNNpWLnRT1sdTDeVudfCj1TKzwCDvNbly93bGIaPwHP2qb4+2Y9++uV+7vjnP/OL/zjITTfBZz8L3/wm/OY3wkZMTMhjrlolTGQ4LJ+LgwfLG9SAPJdgUNZLJoPzBMapZYx6CAYctmy2DKJhQK5DEi9HZpqWbKdajWq8ipdSBvHwYZeRzWQmv9fPexSLKD1vBnvxVSgxBahfFPWZm+wYaVk45jtzjGx28jp8wQv88s1cTootRxMgbtkixSD1WbnjDiju3Ov8XjmYZjIaliVrr9L1p0Ld3qxPSG6jIpOh51D1XkgF3nfvlmv1xBPw29+6vx8fF9DfePfP2Gyuo5d2CgTJhhqILErQ3u6X2V9wAVBT61Tr/sCL6aMNPvEJf8N2pVHKIOIiwtkARPX5dBQk7e1+gJjPle01P97iBECs8lDJwEkn+YfTqoTxqMlMn36aNDFbf66hhUMEg7Bsmec2HoDYyAgUjl8GcWhINqNQSBKe+nq491548EH5fSzZg5YS5JUhhu+I2X/Apy99/PF5PBGbQRxFKn1aSHrYyvZg2JW/BkalgdsGiAumgn+EwpvYqMTUyyAeqdd7+DA888zck9uxraJ5yRKV1HIWDqYgAEftEakULFqd8MmjkoU42dRCsmytLBSDWAoQVcGMbAYjOc6+rS5FlUi41u1bv34vhz7xDaw7f4f17e/Atm0Ui5KL3Hcf3HKLPPbatVI4CIeFhQTpY1QzZ719VUqeFQp5jGrsjGOQFtLECARnZ1IDkgCp1zjeshzwAMSsbPbV2oc4ncQ0l5P3Ua2dv7iKYWgILAsLyNgMoqZpM8rc1LkcbYoTxdU3Z/I6B3ZUZw+wClVoA/f6xeNw6aX+6/nnPwt4mws+qCS2bJH8Qs3t6++Hzfe7CcceujEiMeczVFfnS1UqCuWWacZqqNM9SZZhcGh79WpMvaOdFHP46KMu0ZBMQtDMc+ptn2I7a7DQmKCGTF0ry7vFxfe009zHa2+3c8Ll3YCGic7PuFoK1T/84eyenGVNAohpwzUamknerSIQcGXPjrFYaytLdc9jF4sc2HuUPqBVFCcAYhVHKiUJTig0uQJ2VGWmhQJs28YBlpInLCMuwkHicb+syw8Qk2DIgjseGUS1r4XDwh5OTIhcFICBfoK7txOkQIwMHfRwLTfzfr7KVdzO6syTPlpg13ymEWzfjoXM7gKcOXplGUQPQASOSQbRNP2JTSmDmMkcuWR0/365jNNJkAxDqrfl+liS24VJduzDQ5XPQAT57KlDP58HKxhiUdC/EPt2VN/FLTUZAknEly8HfXQYDvdhDg6y93991bFZ1zSbRXzgAf7wd7fxR/MCfsB13Mqruf/WAZ54OMfevcJE9PbKe+y1UG9uFjm96ucEfzFOgVOfUY3NIA7QSpo4elh3nnOlALG+3n2NffoS0HUXIOZyYBpV62Tqff9Ke9g0TQos+TzPS5FqbGwGJtZ+k3NExKREl4UViUw/LkGxK5YWoCPmX3vPPVil1K8d5ZQYsRi89KX+6/nss3Itj9Y1fOopWQqqyDc2BvfcKdUbCwGI43oC0xTM0d4O69bN7m+oPde0NOIJ/wXv2VK9yU3pyCeQ/U2ZcmWzkP3JrWT7R9nOGgIYFLQoRryO7m7Zx1avdh9veBje8AYkIV0sxni/5uWMUi9NorOxCh0d9W0SVjRGuhB0/l2pSY2muYUa07QLe4EAXa1+NuXAznzVs/rzjRMAsYpD9UY1N/sX8y9+ITLETOYoMYg7dkA+z3akjG5pAQKhAMuXl1hFr1njnAxNDIFpYJnmcQcQDcNlGUIhuV5f/7rNoo6NwtZthCjyd3yFF/FHTg9sYfVpcV7Hz/kI/84XuN5mH2S32r9/HtXXLVsYp9axpA5EApNMc5ywAWLCYRCLx1wPYj7vMj/ewepeBtHbozjXsCy3WFNulpSKffvk+m7ZMlkePrpTEkinf9QG95UY1MDkgzGbhfa4n8rvfa76AOJUDGLXnvsIJF06bc9EM1xyCXzuc2CaLH7oNgqf+Fe+a13HLrqx0IiTwiwYDD+2i/vuk310+/ZJrS8ArFzpJiTKQAU84BPPqIv+FCSTpImRJk6eKHpQQMVsTGo6OlyQf+BwGJYuLRl1ka1aiakC2qUMoho983wZ1ViWtNg/88w0DsaeERcGOgT0iqTCDkC0YHGD/3B+7vHqW3veyGYng4tIRIzzvMoi04T77z96KqKnnpLnoQygDMPiV/fKhelnERPUMKYJwgsGxbjKay5VSahikWFAtNGva+zZdTTdAY9uqGJb6agZ7/438tWbuZeLyRMhSJF8XQuRqE5bmxTlvMXmoSG47DJboWQniDki3Mpr5LD71rcqf3Ilm3Bv20Ysy90oVGtVJeE9BxVGbe2MEPGw+hMjhWNOLTXbOAEQqziUlEgl9/k83Hyz6MYPHoRNm44Sg2g7mO7iJIEswRCBgK8XWSIaFe0rHhlUoXjcAcR0WpLEUEg2sOFhj67/uR1gmbyZH3ERDwAahZe8lN1X3+Dcv55xWsZ3OydqsVh+FtuMYTtrHHJGk0AwGnbmHE0KmwZpZNhnUnMsbZq53OQRCd7vlWvbfEddpNMuKMzlyrMTStYIAky9LmqGAeP7htGwxAETIBgkGq0cIIILaCxL/sbiOv8G0bu7+gaUlmMQY/37WPrJdxLAlczuY5lkBDfeCBddRPiav+KH1rX00o5BgCBFosiFLg4miRYEhfT0SNGttEgQCIjsNBJx39dcDn73O7j+ekmCVQ9ieo+U4AeQ2WCFcA2anc3MpgfRCxAPHgRr5eRZiNUoMfUmagogehnbUifT8fEj5OpcJsbG3D1hSrWEDRDHqJO9VK8MIHrHlLS1+l/A9m3VJ+/2hgKIXnChEvGXvcy9nWHImJlSJUU6DT/4gWCGuRqDDQ25+6YqLJDNsj2/nH0sZTcrmNDqMWK1GIYwgWvWVM7gq3AYRBNCzf6L3nOgeq9jqaO3iv377T22t5fkYzv4LXJBAxjkaxN0dsp9li+X80jtUePjcr+rr0Y+DLaL4c95rRSpP/YxkZpWsphLPjCbai7Csty7qvFulYR3HTr7zpIOluCZbZE/YVRzAiBWaRSLkqhrmgDEYhF++lPXiCEYlI3yqABE28F0D8vlcAwFCQb9phBO2DJTlcRYxeJx14OYSsm1CoflOv3yl55fTIyzjH1cw49Zyza44HyK7V3sN5aQv/LVzmOsZJcPFe7ePYcnsnMn5PMcYgmgiS17KDD1DCj7gjYxDFhgGhiGeUxJTLNZ94AoN9xZ00RNNl+AqBwa1aFbbsZST498PlRStW+f+9zGxsDq7UPDZAi5YFooSDhcucQU/AdjJgPtjf4X1rf/L+3+MftQDK+T1JgG0ff9NYvGdxLBfX1j1PMlPsA4tfDgg3zbfBsPcR4ABgFqQzk0oEiALDEiffuIRQw0TcZ3bdky+W/X14sRh7pmTz0lxa9AQKTg/f22xHTfABYegBhytVCzkZg2N7vXcHwcxrrWTwKI1SgxLRQmOwlv2OCfZTk2JkVQ5bR4tMYJeAH2TABR9eCjCUCc6TqGwy7L0bS4ZMzM3lBVS9rKMYjqs3rllW7bS6EgX3fd5d6+UICPfEScM2++Gf7pn+zEf2hIKt2eirKaZVruvXrqqclMtJZOY6JzE+9mJysZre0EPUCxKOBw+fIpCqTThBcgBpoT4HENGBjS/vImSnMMb7HNy8aZpo3PHn2U7azmGU4BINBQR8EKK/WoyPpLFElDQ3DVVfbaWLYMdJ1x6vgNV8oivu46uPbamSvPJQDxce0sn8rGK22dKSZJTAHa2+nCU3nP5U8AxL/0EzgRc4vhYdkgGxpkIf/sZ37QEApJAnhUnJhsBnE/XbaDaRmDGhU2QFyM9E9RPP4YRFXxDoXcBBKAw4fRsLieLxCiSP2l57Ho/FWAJEH73vwPzmN0s0cuup0VzakP8ZlnAOh1ZlfKdSvbfwhyui9eTDPCXAGYeeOYkph6HRG9ANErrxkamr9UWyWzigFKJv22/qbpVr5XrxZJjupHBPvs7Osj5ZEHR+IiD54Ng6iSNMuyJaYt/kym91D1DdGb1Pv03HPEtj6GBrRjy5Jq6zACEW7j1VzLzXyWj/F9riNPGLCw6htZel4n/1f/KK/lVi7gT8SMCRITkiGMjMCPfzz1c1CMx8GDrhGVpglTnM9DvmeQERqZoBYdg6zmuinMRmLqla+aJvQ0neoHiNnqlJh6h6yrxLShQVxjwU3g+/td6fXR2oe87NWUANFuykpiL74KHEzBVZEARNsa3BElQGbCqOqE1AsQSxnE7nu/y8mH74XDfRQOD6ElB7n/t+OMHBiHAwf42sf28+yfhgT57drF1p8+xY7FF8lGeOaZ0NUF999PMglvfjNccw285z2TW9ieeEL2c1VkaIxl0AtSJb+Xi7mLyynWJpz94vTTZTzYbKOmRvZx0wSrsdkuokpY6WxZSXo1RLk+UpD1t3dHAZ5+mt/zEufnxbYOEglXRaFyiVKZaTwOr3oVEI7YhjVwC6+niH3o/uQnYo/6pz9N/eQ8ANECNmVO9jGIJ59c+essxyDS0THJybSa1+ORiBMAsUpDVTkbG+G226RPRkVdnZug7N17FCzBbQaxhw7fiIvpGMRWBkSmWCg6RidHy8VsoYXqmclkBKOJiYEF/Yd5Jb9kPVvlhtde68h0i0XY3XQWnH8+YDOI4CD+PXvm8ERsCqQHO8O0r9uUDCJAdzfNngTUKhjHlMS0XFM++BnE4eH5G/MoMFhf7/Tq+1jEvj5Jkmtrpfq6cqUc0IcPy2cn2ZuB5AhJGsgRBTSitZXPQFQxiUFc7C/D9x6uviPBZzLU3wt9fcQQRP8i/gjxGmhuxli9DurqGaeOO7mcFALSAo0N1LbG+fAnE4Te+VZO5wnewXe4kl9TO7wfLSsJ5u9+5/YSl8bwsFTfx8bcvTcYFGAZDsP4oVGRuAIRsuQD7lwStV9XGkpKZRjQEyjpQSwWqlJi6mUQ1ToMh+FUmcPtA4iq4n809qFcTvZr9RymXPd25ugwiHqgIgYxFHIT73xDK6t5zv1lNusMkq/GUMyut4c0GgW+8Q3i738H5w/fTiA7gTU2hrV3PyOP7eTu7ndyT9d13Pbvu2Dz07D9WTiwH4aHuaff4xwzMQFvfSvf+1bRAV/bt3vUOHY88YR8jhRAbBg7QC1Sneuhg6fjL8AKhSkWBeSdc46/MFhphEKyl5om0NhYUqTJVD1AhMkAcf/WCQoGPMy58sNIlFxNs5P3tba6170UIAK87nV27tO1FFat5rDezr1c7N5w71544Qsdd9NsFh57zFNQ97ypu1jJGA1OsSgYlDOz0vCeg14G0T8L8QSDWH3ZwInAstwq56ZNDl4D4MIL4Yor5PtQSM6xI2pUTUW82wAAIABJREFUMzoK+/aRpIFx6mTYr90LpRJfX9gAMU6GEHnH6EQ91PEQw8OyCR08KBtoIACMjtGSPcjf8E25UTQKr32tUzEvFGz26AbpRVyBTSX190M2M28GEWYYcaGiu5sWBu2ORQvzGAOIExPlK6bekTHJ5PzZCgUQa2udNgwOH5ZkxrJcpl+x8LEYLF0q3+/YAWPPCQM/SIsMJQ4EiMbkSc4HIC7u9COTvuFpLBgXaKTT8no0o4C+cwcaFmHk1L9s2Q5e/NpG6uo0CloYzjgdli4Ve3a9AVoXEUnUcN11Nlv8ur8huHwp69jKW/kBIYrEhw6AZZJMigSuXAwPC8gH18VSAcRQCCZ6xp3RMkkSWAF538Ph2THA4DIehgGHCotIkHTHlRQKjI/PzhxwIYQXIKqEPRp1AaJ6T3t7XQnh0WAQ1bna1OS2BJQ9P+0FO6YGdQf0iqTCXolprralBCDmjhmA6DCIjz0A738/AKt5zil0Kubox1wjJmxl4l4u9o17yu85yO+/55dEPfCA+71pujJwXYdI0CB0+BDNDKFhkSPCcLRd9gobzJ9zztxeayAgrJhlgdWQEJd2zxtxaF91Vr/LjXwCCAYsCskJHuVsBmhlhARmRweWpTlFbS8oLAcQGxtVL6omVa4zz+JHjX9HDo9DkGnCu97F7l8+w5vfLL3cb30rHDxgwdatzs0e50yIhMWN25L1OqUSqkx4DW2cNqwTDOKkOAEQqzDUPCjDcNSegGx2L3mJmMVomhyqQ0Mc2Z4UG2TsYxkGQQgEQdPp6vIn2E6sXi3JLFlxiDINjKKUqI4HmWk+LzIzNXRdsQscPsxb+CFxm+ngFa+A+npOOkl+XygIgDh0+lWwbh1d7CdIEZBZQIODc0iQ7GvXTxsgAHHKERcqVqywD1jQANMwnMHkx0JM6l9DDn8vgzg2Jgend87dbMIw5P66LgCtpkZAgWEIqOjvl9/HYn6w3tUl0p3xcTB7+tDt/sMsUbRQ0HFEnKvENJOBps64A6YAUimtqnpMDcMF31p6Ag2DGBkpaMTjNH79M3StDPHqV4sEaSKlY604ify5F5FfuhK9toYrrvCYaITDnPyNvyeqF+jiAJdyN3VFl0K+5Zby4MsLEBXbFQy6bNT4gKxzHVPWn32jaHT2BhmqcGAY0JNtQsOiGTsLs+Ui1SYzLScxjUTgFGl1ct7T4WEpmOj6/NbkVKGS2eZmV/Zbdj3YmaMARKn6BQIzz2HzMoi5eMIPEHPZqjaqmdSDmMkQe/sbnQu7lAOss9UyReSCHmApacrPJuhjseOUDvAg5zOxvcd3+GzebPsZ9Pay/a59jI5azl7ekB/gfPN+IhSoZxQ0HSNaQ7Eo+2Bn5+yKa6Wh1q2pB0nE/B/Enp3VZ/YF5Q2/AELjQ+hGnudYjYVGkkYOWUtYvNg9U7znkPcc8yoaXv96D/CsqWHfqVfxr2ffLk7AdvTlEnz0TQcYOix72fg4fPudf5amVTs2cQZWJOrsxc3Ncl5WGpGIe8Y7rR5lGMSeHvf9OB7jBECswlALTrEPIKzEy14mGODtb4ef/1w2z3wetm07gn/cY1BjEMAKSfXHGUpdGpEIrFpFlKzjEGhlZTM9HoxqUikBGOGwbL6hEGCZMNBPJx6bymuvBeTAWrTIlQX/57d1/udFX2acOreBur8fsGZnVJPLSWYFDGN3kIdCFTGIQUz72llQNI6pURdesKsORLuPHpDDTB0gc33N6v7xuHs4KpngoUMue9jVNVnm6qyrvj5SyEmcJUI47D7H2SQ5KoFVYy605ia3PxigWHCATjVELueCBL2QRwNHXsq//AsrrlhNV5e8l6eeKlMuOjshmw8Qj2tcfLHYsCt5UlcXtFx+BnzoQwBcxw+IkSE0Jk3fQ0Pw/e/7n4MyDOvtdaWl4P4/lYKJQclkmhhmG+ucX0Yis2cQW1vlfoYB2UANIzT6XKKBqpOZZrNub6FK3MJhYR3OPVd+pj7vjz7q3uZIqhlUb7xliYR/yxbXxXjSDe2G4THqheUKVC4xVWs8b4VZ0+K369y5JVeVRjWWVcIgWkXYvJnIiCsL7GI/iUvOpGOJTj7ehB4MYKFjBGNQUwuNjTQtrZVFuHYdnH02933uz86begdXQLHgM1awLHjw+lth+XKeuOJj5A/0oefSgMX65J84l4cBaGYIvSaGYcibX1cnDsTzCXWtDWNyD3HPniqj8O0oxwID6AP9LKbPHQNRU0PRCtLdLes0GvXL5L1F58FBv9PoS9wWRtADPFhzGV/4q0ewgCQNXM8XGEpH7cTVgoEBHrgzxYjd71sgyFPx8ynEGhzw1tExQx5TEmUBYkcHNaRpxGYu8jmKxfIjjo6XeF4AoqZpUU3THtE07SlN07ZomvZJ++ff0zRtj6ZpT9pfG+2fa5qmfVnTtJ2apj2tadoZnsd6q6ZpO+yvtz4fz3+hhTr8vc6KSpr2hS8IfggE5JC77z4xVzhikiMbIO5iJSYaVjCEps3gILV+vcsgAkZO0M/xwCCmUpJgKIAYDALDI1AouFX/RMLRBcfjcnB5GbrNDRfy1cCHKBKQSls2C2PjswOI27c7pbBhGqVnRtdnZhBt/UiclD0L0XB6SI+FUADReyCuWOH2sIDLMk4HEPv6pv69MqjxsgstLXJIZTLyHCIRaGubfN+2NhsA9vY6yWiWGNGYiyTnwyDS3OwauQAUClV1IGaznjElRUGKqhDFmWeiaWJqscYmIuJxmbzzildIT0xXl3gjNDXBRRd5APnHPgaxGJ0c4nLuotYYgZRcyO99zz+jMpl0jai8QEbJIifGLcaTsvaaGGIL60V5gSRWSlZeaUQi8vkxTXlBPXR4AKK8GdXmZJrJuO+pr38NKXiC+34ODrptFd4198QT8D//4w71nm2MjckWOTIiZMWmTQISJ+11AwNOVWKcOkAHTa+IQfQWdvJ5WNwd8xnVpEfyvvE21RLForsONc1C27qVYHqUAO5CafrXvyd+xQtZd8UyjNZ2rFNOgw0bMc4+D84+m5d8eCPv/e7ZsGKlbHw1tdzzRCPW9R9liCYewdaDHjwAOXuN79vLA9/aBvk8j3Gm9B+ODKAdPMhZY3dzNo8CEKZI85IoNTWy1uPx2fWslQsFCk0T4nX+RsZqHXWhxlyAuw41zYKRESLkaaeXFgYJNNTS0CDFqlBIAFqh4Oak8bi7fvN5v+PwBz4wmVD43eCZfPWiW/gYn+MgtoZ+aAiefRa2baVI0BmtsbXmHHLrzyCXlycYicieXlbBNkV4AaKTh7a2gq67LGKxCKZRlevxSMXzxSDmgBdblrUB2AhcoWnaefbvrrcsa6P99aT9s5cBq+yvvwG+DqBpWhPwz8C5wDnAP2ua1vg8vYYFEaqJXtf9h2NHh0wxUJrpWEwWuGEISHzzm+XQm3d10jMDEcAKCgtljzssH+vXE6JADJFdmDkDyzp+AKIyrnAYRDuDacXO4q6+2pmgHInIwXXVVZ73NBLBWHUyKWrpY7FUrPsPz64P0ZaXAozRAIGg0w/Z2jrN/WyAWEPKmYVoGMdO/6i3KV8diG1tkuipA8c05ayaCgA++SR8+cvwpS+VZ25UhdKbPGqa24sIwmpNdcCdcgqsTz/KKAkyxCkQJFojp1ssVrkDpvc5qB5Empv9DGKhWFUAUc3ZAggUpQDlMIj2AgqH4Q1v8Pcbqc9+Q4NrruUzq2hthXe9CxAWsZ4xtNEkYNHTI0BExdDQ5P5DcCvqowN5CqZGiAI5IhzW20HXHbfT2bjvqcdVANGKRDlEpwsQjSJYZtVJTL0z9LwSUxBwf+GFfobil7+Us0ytyaEh+dnmzXDHHXN7Dmrtqp7DSESMEycmSs5NT2PSOHVOP2klPYhOiwF20XbZMlaxw71BNnt0nMePcuTznkLN2AgMD7nrEOCNb0T7x39g6VJhkerrXSBimlKo+chHZGSMd2j94cOw/aqPcFfD1c7sXkwT9uyFvXtgzx4e4yzGqWEzp8nfxyRspDiVZ1jEAMvYh7aolXhDmLo62TPD4TJzm2cZXoAYS4R9v+vp1aqSCS7HIJ61bBAKUhDRgLpAhs6VEa66Sva7QADOOktuq4opmla+DxHkffv85yd7Vvxcex3b6872/7Cvz6kc3c5VWHqATe/+JtTUkMvJr5qbp3DQnyaWLHH3+x077PUdCMDixSV9iMe3Uc3zAhAtCVVDCNlf0y2fVwE/sO/3EJDQNK0deClwl2VZw5ZljQB3AVcczee+0EI10ScS+KRgHR1w773uv0MhN1kxDFFlfOYz8N//PY8/blklMxCBoPSxzQQQNaAWO1MuFo6bURfKwRTkOoT0IgwOECXrHqC2vFRFLCbSqle+Ev76r+3N75RTaGSYPGFMdOgfYPeuWZxAdvd+jrD0fIQkqQmFZpAodnZCMGg7wVlgmRiF4jHDIKqeC++BGI/L4aUOEF2XtTbVcO6bbpIZpD/8Yfn15TWo8UZ7u/yNcNgPFksjFIL6g1vppZ0kCTQgUicJybJlflnqTDEJIDaVSEyrjEFULrSaZaCbkqHGyEh27/Gv13WR4F9+uf/+GzZM8/595CMQDLKEHq7kt8SLo87F/NrX3M/C8LArQ/KCGPX9yECBGlKczSMcYClDAaGKFQCai9RNJVeGpdMTP6nEybRYdRLTcu6JXqDw9rf7wfehQzIyaHzcxgt73Osx1Yy8mUKdreq+gYA7e9E3B9WTMU5Qi2VvFLo+M4Oo6+7rME0oLu1mER5r3Gy2Ks9FFyBa6El5AQ6Tf9ZZ8J3vgKY5MvozznABZTgM//Ivcu7F4yIp9sYfHorzu3V/7/9hX6+4XgIFQtzFZexnGRomGhAlx8k8C8A5PAJLOolE5G8pFcV8AWK97U9kmmDUJnxMcCFbrLoijRp9VNpycWX8Xv8NEwmCIZ0Pfxg++lHZJtUe5s0LpgKI6nf/5/+UqF90XUwNg7JANvAUIVwL/l7aeexjP+PxrBgf5nLynJubZ8g/y8TSpe7nIJn0qA7K9CFWY8HmSMXz1oOoaVpA07QngX4E5D1s/+rfbBnpFzVNs49MloD3KnHQ/tlUPy/39/5G07THNE17bKDa9DbThFpo4bCbeEYiAijuuce9naa5Gxi4Tk2/+tU8/vjBgzA6yji1DNKChbh5zJTgKqeBegQpWXkBiMd6D6JluQyi2nSDY0Ngmq4zaGenaNs8oTaudFo2sre9DRZdsIrGoFzwIkHI59jzxIhP6jZt2AziAC0YBMCWBnutqctGMAhdXdThakTMTP6Y6UEsZ1ITjwt48/Yhjoz4DVFU9PXB3Xe7dtnf/e7k5LQcgwiyhs86S8Z8zWS1fmB3AQuNERKEyBOIi35ntkmOeg4qGZgkMa2yHkRnTIlpOE6eMTKuTtgTmiZTY17/egHrbW1w3nllHlRFVxe85S0AvIUfkiBpU+cWe/fK2Aso72AK8ud1HUaGLIoEKRBmE2dQCMgxF436x57MJtQsRMOA3mg3TXgysEKh6iSm0zGIIJfTnvgDyG3vvFNAxsSED7ORzXqcCWfx91MpWYdeMKhMqnwFMU/GOEGts3grYRC9r8uyIL+k2zdDj2zWN4exWiKXswGfYaIjh12UrNBFt93m2Ccrg6Xly6VY88pXwo03uvtYMumyUSpuvx32hlc5B6OOyUu4GxMNA50iAX4UeBvppk4CMdkX2zjsFE3OWTMKDfWOBFjJwGfLOpWGl0GciLbQgWeQe75QOtd9wUexKOvKMPwtFmft/KnbDgPQ2MQ55wgwU4C7nKHTdAARhMX7/OfdfAeQTXHdOtbwHJ/h41yCJ6nt6uKn2dewbZs8x3xe3vvW1tkDRC+pYRieMXEnnEx98bwBRMuyDMuyNgKdwDmapp0C3AicDJwNNAE32Dcvl7Ja0/y83N/7pmVZZ1mWdVbrtBq66gkv6+Y9xNrbZXC6t/IfCkmVW/W3qCT1wIF59I/Z8tL9dNkGNSFAo7V1hgR31SoIh8VJDMAyMXOFqqyUziZU0qEMQXQdAv2SSToV/ze9aVIi6wWIYA/8bQsTO7mLCDnHAS53cLDyQ8hmEA/TJgxkUIasV7Q0urtpICk9iICZq85Za+XC23OhLkMs5g60V6EktaXA+N57/f2ifX2u1Tq4iVMw6E94VcRi5X/ui2SSfeOipE+SIEoeIkKvlJ09Ok2oz5bDIDY00K55mrYMg95D1dM/44wpKRYdgBglO22D0bp1UvV+z3tKkpNyccMNoGl00MtV/JpoQQaaWhZ89auS5O/e7fbYBIPutVaGNbmsRYY4QzTzJBt9DqZr186OAVbR1CSfHcOAfLTeV2mnUH3shUr2YHIPooo3v9n9XaEgSeemTc7kJdJp+fKOgao0vOMtvIXLSET+ju/M9DGINaDL9QwEKgOI6nWZJmQWHxsA0XG/NIvo9jkRJStZ+BK3hu/dVxsaRFKqwMXOnbKmHn7Y7xyZyQCa7jSurWY7BYKMkGCMesb1BnYtfwmFUA16SzMs6WTj+iJaXR2cdBKn/vAGwmH/Iuvs9DPUcwkvgzgRapzUy11tAFEVaUzTk5JYJvEH7uQyXAdRGht5xSv89/UCRFUg9ZrGTLUfrVolyjZvYa3ztCY+d8MIg7QQJSOKp9ZWWLGCTZv8w+1VMbepafavV0n7fQCxvd01A4Tjfhbi8+5iallWErgXuMKyrF5bRpoDvguqC5mDwFLP3TqBnml+flzE6Kh8mGtq/BWZUnkpwNlnu+xEba2/R+DZZ+f4BB56CIC9LJcRF7aDqapmTxmhEKxdS8IGiBZgptLHPIOoBmc7DqZ6Ec1GxQ5ALJGXgjurzjt/q7ERtFNOoYkRiti76UA/u3dUMG8ilUI52hxiifRyBIMEg5UDRK+EzchWH0MxVXiZi1KJqRe3TwUQvay9ittuc7+fij2cVezd6wxZT5IgGnFrZbMFiOogVyY1lqbT3pj13abvQLFq+mcckyGj6GcQZygpVwzK1qwRNxvgDfwXDYxCUj4MW7ZIMutp7yUUEpWUYkRCIcAoMkIj21kjM0jttTeX/kMV4bDfqCbvnSVWqL4CjpKYetehk8BbFjz6KOsG7uOkDnGoLBYFaNx6q7SKfuUrMoLkllvgD3+YvVGNer9qa/2GGuHw9AAxRY2PQaxknSsQaVmQalnmB4i56gSIzvUzTX+hpiQ5CAZdtVEgIO9tsSjv7y9+Id+HQuUNu2hpwaqT+ct76WacOgwtRGDVSsxYLYGAfH7C8RCnfexKQfrPPUfk7NPYsMH/UFO6rs8ivAAxFWqg3ZuKFgocOjT/v/F8hipmW5YL4iOZJHp6grfyfS7kAZqiaa59V5wXvMB/30hEvrxzQ2diEFVs2ABf/KIUC664Av7f/4O6T32UW176HZJtaym0LsFYvRYvP6T6D5ua5PNUWkyqJNascX06enrsNd7RwWL6CGBXKPJ5hodnr0g4VuL5cjFt1TQtYX8fAy4FnrX7CtE0TQNeDaij9pfAdbab6XnAqGVZvcDvgMs1TWu0zWkut392XIRKylta/Gxhe/tkgHjxxbLRrlghOm/Lcj/kcx57YWfDMgMxgBWWVVnR/JlTTvEdhGY6c8wziJmMbDrKoCaYFnka2ABx/Xo47bRJ91OshjdRSSSAlStpDE44DCKFArturwDtb9vmlPX2sgz0EGi6Y3YxY6xYQSsDzvZs5o8dgDgVg+iVmILL3HsBYjYrBjWl4V2LCiCGQuIw/MEPwv33z+45FnbstYG9DRA9DqazBYjKvc00XVlsbXOEOO4JmE8XqyZJdZiLYmFWAHFWceONAKxgDxdzL5H8KIwMYyWTfPFdW9j1+z2QyzqM4emnuwRmMAgUBSA+xQYGaYVA0GEY52q1HwpJAmYYQCxGxjtLrljwWctXQ6iED0okppYlGvtzzqH+1Zdw+oNfQd+3B2PfQfStT5Pe3cP+7WkMw32xBw74izQzhVeZU2oU5WUQnffTAxDTxCGgO9LYShhEZ36eCanGzskM4lAVXTg7nB5S03CUJuUAIvjzBVUk/vWv/SC8fOFSI7R+FbGQ9BkuC/aSaj+JYIOAQzVCJBq1Cy8eneQFF/gf6dRT5/pK3VBmN6YJZrTG3wdchQyi08/tkXnHRqXSEiXHp/gE/3P1f/HOd2llC2ylMlMvq6daNKaK9euFSbzhBtnXduzSmTjvUrR3/y2rX7yUibRfoubtP5xrL2l9vRQi1PN67jmgo4MghssG54WqPF5ZxOeLQWwH7tE07WngUaQH8XbgZk3TNgObgRbg0/btfwPsBnYC/wm8F8CyrGHgU/ZjPAr8q/2zYz4sy6Xpm5vxbT7ZrP/fwaDbr7FunQsQVWVn69Y5PIFUCh55BBAGsUgAy9bGVWQXfeqpTp+MhYaZyjozp47VyGaFeXIcTLMu4mthEN74xrJURm2t3CeTca9ZIgEEgzR21boAEdj9+z0zPxGP5nEP3RCUmV0qyZwxurttx1UlMTWOKYA46VAsIzEdHpZ8Q/UsgsjbylUW9+xx16MCiD/5CfzmN6LS/rd/m7qi2tcnRjcPPugy/oeeHsIgQJoaNCAQF2olHq+QAfZEICBJr0rGs1nQWiaPuqiWPkQ/g+hJTOfrYe+NM86Al74UgGv4ibCIY6OQHOGRZ+Ls31WAvl4C6TF0XQCiYihcgJggSQMjNEIw4FS81fiN2YZiEA0DiMcZptk1vSoUKBSqa1bppCHr2ADxa1+DH/wAAB2LpRyU4fJGAWN0HO1wH9m9PVgHDsLwEGQzgMXtt9vjYiuIZFLWQ23t5B7jQED2cC8zorJFA50sMWejCAQqkCxTAhC1WprqPZmzZTHckyl/xwUclTKI4PYhguyrhw7B44/7b9PUVN7Ved05dZiv+yu49DKWv/5czFCU/n5/MTUanbyurrpKQKKui7Lqqqvm+krdCAZdmTfxuOwLKqoQIHrPNvXeR4dKZjxceumU9y8FiOGwn2WdjWLsqafc71eskP1B5Ypq5qZlyedkrrXAcFg+i4Yhj7V9O87n1ZGZ5sS99QRAPIphWdbTlmWdblnWaZZlnWJZ1r/aP3+xZVmn2j97s3I6tWWn77Msa6X9+8c8j/Udy7JOsr+++3w8/4UQyk0tGpXFphLTaFTmP3njnI15anKCm9evdwGiGka8descgNmf/uRkrPtYhhGMYtm9FxXJpE49lQbcuUhmOkuh4JdRHmsxiUHMudlHC4NTOmRomlt987rWAjSessQHEHdtzc485NKjgTvIUgiFHIBYEYPY3c1iXM2WUTg2AKLqZSh1T/RJTA0DUhMM7xgk9l/fhW99i7H3fxw++EH+/Km7yQ8kIZO2Tyz5bBuGWO6DJC4jI8IaKrfLsbHyzKNlCTh85hkx4PjqV8U0eO8W+dwkSRAh6+hpli+fff+arvtNMsrOQixWj5OpwyAWCui2LOiIM4gAH/84AKfxNGewiTCSOFhA3pZ8hwZ6CVtZ1q51q9pBMwf5AiM0MkyTeCwGg0SjkovMZoalN9TaVQxiH4tdo5qCZHnV1IeoGESvxDSy51lpFvVEE8Ns4ClqmcAgSJAiBcKYpgnjY3C4Dw73k0mZ/Md/VPa3VbGmubm8s7YaKTU+jmTQduafJi7XUxcGMR6vbD36JKYpaFxW7/v9cG++6gqnDoNouAxijMyMAHFkRIphxZJOiWCwfPGksxOMSA0sWULXyhDBoLyP3vt3d08e/RMIwKc/LSNQvvCF+fcfqucYi9nFtpoan5EbhXzVAUQvgyhnX5FYKUD0Tbn3h3rPvYWpSmWm3kinbTbPjnBY2lhVmlMoyHteXy9fHrPqWYXutrVimtKFU2gV/bNjVHOCQTwR1RDqsG9p8bOF7e0y59CJgX4u/upfCbXw8Y/Ttsiis9OVlWWzctDNWh9vy0vTxOhnEUasxjnEKgKIp5xCDWk7sdIwsznAOqZlptmsbJahEKTTFqGsq6FpYXDaN05trGpTVYlkYn0nRT3sODP1FlrI/PKuSff3hQcg7qXbmYEYClXQPwrQ3c0iDjuVYbNgMDpqqVnRVRuqYFLq2haPQyRs0TixT4YyDw5Q7O2n8LVvws0/YuwbP8b68pd58I4khcPD0H8Y+nqI798uLIZpcOedrqz7/vvl8B0dlb85OCizl0pj3z5/Uj86KvP2/rhFPgxiUOMHiHMJn0mGPeqilEGsFoCYyYCRN2TMhZKY6rn5WxSWxkUXwatehYaHRSyJoJXjlEN3OtJ+sAju2QGYjJJggEWgBwiEA4RCc+8/BEmaolGbsYrHKRJ0ZcJ2Ia/aAKKPQTQNIu/9a3/xKxqlMSHA4yp+zVv5Ph/m3zmHR3g5v+F8/iy3y6YpHB7ivvvg0Uen/7ulypxy51E4LLcZH0cOX5t+n6AWUwuLgQqVyUvBz6qk01CzrMVnMpRLFaqucOowiIYxI4NYU+Oeb5ome2OhIJ9lr4NpaetKV5drzhcIwDveMRlEhkLTy0e9ZijzjVDIAxDjccLk3OtYKDA+Pg9DwL9AqBEXjpommSRmeSQya9f6DIdKQwFE79zQSoxqSmPLlsly1DVrXJZYsYmNjbBokd+tf7bR0iJmSYYhr313XtCmAxBtBvF4HXVxAiBWSSjGprXVDxAty/PvsVGCWzdzfvb3smt99rPon/k0XV2yiCwLJ6mftczUBoj76cJEw4zEsSw5FCuSuS1dSjyuESYnJjUmkM0d0wBRmdQEg5AeKxIyXTOQlnhm2s1WMYjJpGxe0ah8BcM6dYmgjKqwY8/37pviUeywJaYT1NBPK4QEIIbDsHp1BS+ktZV4TCNuS9hMgEKhqhLQcqEO71KAGBvvhyuvpGP7PTiyWnSnz2uMenZyku0IK3cKUaCWMWExDh5k6x8H6d2VIp2GP//ZNbkBWYObNk1+PqVKABXmiNzZBYjyPOYKEJUJkpdB9M9CLFYVQCymc2jgMhfGla6gAAAgAElEQVSL6o9sJgjy4fjxj+E73+HCG1/I6nVhQok6iLuuJCGKnL7zFrj1VhoaoGl0D8FRWSQGOrtZAQ0JIlF9Xv2H4L68piacC+okp8XqBYgOe7FrF5FnS2j2b3+bxt/cDDd+nOh73sGyv38day5ZQp2ewUTnAh6gzVY6mOMTGP2DfOlLTFvIGh+Xvx2JyBnp7b1dtEj+73MyLZmBaAaFitK0yazVVOGTmKZAa23x9yHmC1XTA6zCkQibxRl7EMEFf+pzXCzKnFJv0aS+3r9G3vAGVwJcXy81oPe/393PQL6fT+FlNuFjEMNhUoGEW2gzTTCNqtlHwTMyCHsNDo+4snWYVl4KfsCsrtNcGESvvFRZNLS0uOo5b//h0qX+2bOzDa/MFOC5oWbQdRcgFoWuPHhw6sc4luMEQKyCmJiQDVhpur0Acd8++5t8Dp7ZwjnWQ9R4DCf4xCdof/YeR2aq+ppmBRDHx+ExUfmKg2kAKyI0RGNjBTb9AJpGzcrFLoOIDqmJYxYg5vOuKxhAPuWaaAA0r100rR5JDbD3Gig4MtNJfYi7JzfPqBgddZKaLawXB9RgEF0Xi+mK3L80jWjXIuLI3zAJQCZb9TJTZYlfLHr6XYaGiJ+/Ee64gyW4NLtBgD5kYN0Y9fyZ83zOkWt5lvVsEbhomeSGxrnjjI/z8E2bGB21JkmovIcgyPr2jsd429vgwgshGLCc5o0MMYIYEJvbDEQV3lEX1T4LMZ0GI5MHLJdBXDIHz/NKIh6Ht78d/TOf5o2f20jDyhapjoVlAwxSYCNPwvveB08+yYpnfomOJdcMGIp0QH2ds+bmyyCC7L++DBmqkkHM5z0MYlIa0yJ42MO3vAWuuUYAcTgMixYxcvpLGPmH/wtvehP5F7+MrrY8L+fXjplW8bk9HNqd46c/nfrveguv4GcQ18s8bsfJdGICrP1+B1Mz4O7DlTIZXifhVApobvYDxGL1AsRKGERwZaYque/okHHJnZ3usTgwAP/7f8PnPgc/+pGABLWPLl0qt7voIrlvY6O8/4nE/AovswkfQNQ0Jho6qnoWojJsc4o0IyUA8bLLZnyM0j7E2QLEwUEcMBYIiKupWpvnnitbWy4ntfXGxvm70U4CiLsCWIvays5CrDbZ95GIEwCxCsIrLwUXIFqWrR60THhmC+RzXMy9k+6/5FufJJHv9zGIs3Iyvf9+ZwXtYxlGpBbsg3HGQeueiK9ZSpi8MIjokEods6MuMhmXPcxkIGjknMQlQZLgupmpO8UilspMG5c1UAy6bgi7cp3iglIuPKjjcc7ADMjsSnvySMURXdZGrV14MNEhm6l6gOgF8LoO7N1DcPMmggMCllayS36p6Zh1jfzXyn8k/9Z3UXzvB7n/nP9FvrUTamohGuOs2u1cwr3SI4jwjr8Zv5B7f5Ek1T8ZvPf3+2Urmze7yU97u7CDl14K73/TEBsLj7CIwwSwkaxddj+SDGK1mtRks2BkCmhegNh19OfeXn65JCnBoGZvzBq1THAyz0qj6QteQHdBGmmCFESGaN8uGsUp0Mw1FPOSSIAVk73A8LgbQ3UBRCWp1Mwi2s4d6Jiu1fyKFdKQiw2I7VAGJ+F4EJZ20fzpv2ed/hxrkKFmBQPYto2bf2RNyeR4AWKpkYYCiIGA7OXFImR2uw8kDKJciNkwiL7xCCmgpYRBLFTfjOBMBoyiBVYJQFy8uOztVf90MChJupKWRqMuc2tZcn3OPVfW2v797h6p9r7aWtknEwkBI11dczd+mm0oUyLLkq+J2nY/QKyyURdOHymgFfOQTrkAMRCAF71oxsdQn23Vh+gFiAcPCs8wHdDyFk5Xr5b39/TT5d8tLcIiv+IVMr4tEJi/F1k4LGtf7afj49DTuoEESWrsgjj5PLkcVZ/vzCVOAMQqCO94i5ERV4efydi/27FT5KUUOZ8HJ91/ibWfxBP3YOXyFAqyQHftmtnbxAnPsLe9LKdYn3AWeUU9bHbUrO8mTA7QMNBhIlV1B2Gl4e0/zGQgVHQrcc0MVUQfePsQLcsFiE3NGsUmNwnezQp3andpeADiU2zAshOacHh2DEb0pE7idhO+iY6Vrn6A6AxZ1+wDce9eX8X05fyaSFSHjiWYTS0MtK7jt43XMvGyq9ka3EChdQm0tELbYi767w9w4T9cTCzg6tmeZCMHWYI5IcA6GHQZW8sSV1MVXsmpOhABEiN7eDW/4HX8XFw6ozFA8/XxzDYUQPT2IPolpgUOH57elnwhhAK4RlYAkQMQu8snpUcyIhG4+mpJLqJ1YRrbwpzJJoctJJtlBTJ7NEhRkE0whK7L2uvuntvsLhW6Lp+npiYwI3JB84RE8lyFJjWOG/DYKLqRJ4JdUAsGpfhlZ55egDg6KkmnUrDUn7ee5re/ktPZRJScqCWSI+R3HeDrX5/8N0uVOePj7mderS8111DTbKOaPe6bOkENpuZKmStlECcBxFIGsVClDGJWCjWOxLQ2NKW8qKlJ2KGVK0VaGo+7eY2391AV0SxL1FIKIHqZow98QMYJX3mlGNHMR3I42/Bey4maNp/qpNqcTH2zSFNCATrn4bnnVvQBL2UQEwl3DRWLcPvt8P3vl2cTLUtcvlWo2ZUbNriO4iMj8jRyOVmfTfMUi4TDspd6jZOeqzkdDW8f4vFrVHMCIC7wyGTkEAkGZbF5N5zhYdD6eqBHNqVzeETkpZdfLnaItm6uiRES5iCx4YNYtgW6YfidoqYND0DcxzKMmgYHi1Q0A9GO2GmrHNmQZUtMj1UGUZkBBYP29cu5st8WBiui72pr5XzN5yWZcYxqElBsdLu/d7ES66mnxGm2NDwGNVtZB0G3/3BWAHHdCptBtAQgptJVDxDViAQAPS8HoWP0EQiw+JPv4+V/2+lkHOk0PPQQ/OpXcj/Fxjc3w5pTI3R/6h10v2wt1DcAIglNUYuWy4Bp0NDgP2P/9CdZh7297lxTw5DE3pGO75ExJntZLv+Ozd3BVIVXYqoYxCg5EtiLsVDENBd+xVRdAzNfRMMFiNEVHc/L33/Na4StaG+HxpPbuLbrAd/vHYBYG3Myp/nOP/RGOCxf8RYBiDGy0ptcKABWVQFEBQ60bBYNy5WXfvKTcM45zu1CITcJVXM8IxE3CW167xuJLF/CmTxGQTGqe3bzwG/HfH3A4C+8apq//7CxUX6mhrormen4XjezTZLA8oy4mG0PItgqhqYWGvFUSqsYIIJnHTZNP/Pj3HPhuutcqbz6DHiTdZWUj4zIeVosymfA61xZUwPvfCdcf/3cVRVzDXUtDQMmoi0lDGJ1OZl6GUR9XBaLAxBn6D9UUVvrGg+p3v5rrvEXM/fuha9/3T3/vD9XazQedxUWNTV+Vtg0ZYtra3PX/VxDSfW9n6ftQZEOOKMu7IP+eOxDPAEQF3ioQ6xZemd9UpmBXaM+lPdC/ii77U9+Aq97nSPLiZGhkREazGHo76eQlw28IplpMum4Z+QI08dijHidAxBnsyFHTltD1D74TQKQTjMyuMBpijlGJuPOQMxkIJQrmYFYYYboZREVQKythUA04miOU9RwmDbnevvCBohDNDFAiwMQY7HZ6feD69dQ73FuNFPHRg+i03Nh21k7APHd74ZPfIK/flfAed8zGUkEH35YDigFLjs7Xan1ZVeGCbQ0YQVCGAQw0dEwCWYneOMb5eBTFe6eHukFVuxhoQB/+IPYsL/jHfYoDBsg7sHOoqLz6z8EFyCq0TfqQ+bITG2Tk4VusJDJ2C60ecPHXMRWL53hnkcmamrgK1+BT30KfnhzgI0/+LDv98vYhxYIEFvdCbbAXCU0RwIgKllUtC4MeoA4aQGIlgmmWZUAkaKYnKgxIrzjHZNu62URQQDiokWy3za1BuA1r+Gk6CHqsXVuloW1bTuPPepXWEzXf6iYCaWQiUSkcDN6yLWlHKXBoTY0rXIXU10vKdLUtlY9g5jLgZEr+tdhS2XZu2LSlczYCxAPHpT3SDGJxaJc60rmTT4f4WMQI82TJKbVBhDlPLTQxuSsj9otE5UCxEBA9jjLclnEJUvkOL3wQrfXv1iEu+6S/fPJJ+X988pLTznFHT2RyfhVNUr5tnx5+VmZswkFEBcvdlnKvsASRqm3Zz/jOJmWFpiOhzgBEBd4TNV/WCzCwJYBn6zwjOg2uPVW93R7z3vgH/6BCDmaGKaeMaxCnvx+yfwqMqr54x+dRq39dGHFazG0EJYlIEP1C1QS0fZGorbixEAHy2Jkz7FJIWazLvObHi0QLHoYRH2kYvF8OYCoaWJg43VB/RMXyEyE0qzelpg+y8liqhKUGYirV89SinPyyTQx7Bz+xXSOgf7q7tr2M4hyEDoVUzuDX7VKjBBAComWJe+/mtsGYquukvUXvlDWhWUjAc32Ob0w9AiXXurvVRoeFkZy82b59+7dbkXVNOFb30K04HgYxHk6mIILUrwSU/AARLuHbaH3IY6PA4aBYRjoWE6P7/MFEEGu5YUX2kvxRS+Cd73L+V2EPJ3ndhCtj9LeLkmIAhFHwmlRJTeRqAzhi5ERZQZAoUAyOXm+3EIM53NomYiA3ZTENBQqe8CUyspCIXn/1agE6uvRXvUqFtPnjAMiNcEjP3cpgFRKCkShkLuvegGiAqGKQYxEpFY63psihaCTURqwdJdBnA2b4ZV5p+LHCEDMy+blMIiLKtPcqvdCFQkSCXePzOWkX3vfPtdQbPHi+cmzj2TU1MiebpqQCTfQSr9zRlIoMDDgGgMu9HCcaAsF9KKgsBgZeZHnnlvx45T2IYKss0svle3R25aUTMJtt8F//Ic/H924EXbulMLA5s3iWKseVwHE+fYfgn8epqOGq6ujl3bqsBGuXTyuppElRypOAMQFHLmcLDJdl0PRO9JiYMAikHTlLu300vq1T7rCbRWf+hTa1VfTxmESjGKhUehPwmiyMoDokZfuoRsaExgGzoiL0mrudBGNQqxRdnZTOqpI7h+b/k5VGpmMJCDBIKQG09KHZEdLR7hiG/7GRrn+4+P+qmlbG1iJRrANKn7HS+X0/OY33RsNDMBhsX3fxskUCEMwgKa59tEVR1sbLRHXbMVAY+BAlQ3rKglfz0VOshOHQbQ1LeEwnHeeOw4km5VkUMkbIxH/3K116+TaGFEFEC1CFHhf3z9xUpNkfUqGowxmVWI0MuJPfLZssdj3a2GAHQYxPn+AWM6kBkoB4tTGHgslJibkPyYBdNX7F4kQTfwFs8evfEWaol7wArjpJlZcKllMNCrvu6bJ90dCCqeSm2gUiMXQMYmVzEKs1Fr+LxmOg6kH6EewrQrLUATlzpyTTpL/Fwr2+3HyySxeFvONA3rkjiGnnuqdfaik2qUSU3CTWV2X+YTWaJJe5Idjc2QQwQWTlgWpSNMxARCLJUx+pC1R0X1LGURNmywz3bfPLZ5N8bH4i0Qk4o5fIB4nT8RlngoFLGvhKzFUOHMQC3m3n5uMVMC8SGqGUECuHKBqbxc58Etf6s9nhobclo3WVllLdupCOi1gUbGI+bysn44j0EmgXlY+76lF1dYySIurQLAZxLFjM1WdNhbIMjsR5UIdYk1Ncg4ND7vVk5F9YwQKLiu1MbgF3vSmyQ+iaXDTTXQ0y5w2DYs8Idi2jYG+4swyJA9A3MypkGh0AGJd3eyahAMBqLH7ZUz7KBnpzc5wr+oLVRHPZm2J6UjOdeQDWlZUPtlV191kJZVyD9Ply8E0NegUFnEHq9jDcrjpJnen/fnPncd5ig2YwQigEQjMASBqGi0dYYelMdEZ6c1WBUMxVbgA0ULPSXbiMIiepofVq8U1LRq1B1vX4KyB+np/wlosCmA0tDAEAgQwOJVnOMt8mLrf30Zrq3wm4nH5nCjZimnKAeTrKxwf546eU7GQ3l903aE7jiiDGI9DJOIa1VgWGOaCT2xSKWBsjCIBd4RMLDZp6sPzGpEIfOlL8OCD8Ld/y4qVkxtFV68+MgmuqjGFQjiov1Y571WRUY2zDk3TARdh8n6U4IlSgOgF3GNjruqi9ZxuQp7CXPLAODu3yt5YKi+F8gxifb27XsK5ccaod+afjtV0YFlyfQOBuQFE04RUKFEy5qLIyLBZ/o4LNHI5MEsYxFh7ZQCxlEEEv7fB1q2S+xSL8j57+8X+0uEbdVFTwwS1k5QY1SAztSx3FimG4cqEyVQ4LNmNcgyiNwIBqZ998IPw4hdPZoM3bHA6K2htldv397vMcS4n671SU6jpIhSSvbhY9OwrdXUM0EqtbcqnGMSJifKPcSzHCYC4gKP0EPNuNGN7h/GmHqedHphad9HURPvnP0QAkwZGKRLCzOZgx47p+xCHhnzC8KfYAImEU8lrbJy9k2L9EvcUNdEZGy4ueLfE2UY26zJNmgb5ibzvWjWvnYUul/Iy03hc2CqveP5OLhdd4K23whe/KMJ/ZOTCVtY7Q51na1CjomZpk9OTYBDASqWrgqGYKlQPIoaJZsphHiMjJ75HvrtqleT9Z5/tJjHBoFzb5ma5Lum0HCCPPy6W7bW1EKoJU0uKD/FFuf4/+5nT96kON8UUDA+7PRBO9A9wB1dwkE5yRKCpGQLBWRdmSmPSHET7hZSOuqgGgGiOjWOh2XoE0OPRSsn55yXK9YoeqTltjsQ0gnNRSxnEagGIpgmYRec6RshNiQRKP/tLl7rJ3diY+3t9zSpWhj3Wg8UiD//n02QyslaDQT/YLNeD6DWqiWRGyRCjQIhBWpiIu/u4rs8DIOaCNDX4AeFIf7Fq5q4psyCjKOyv42LaUdkmVcoggr82oMBCsSi50HyNSY5k+ABiPM4EtW4fYr56AKJh4JgXakbRzyB6zsJKIhaT9yWXm94pPxKRlowPflD+H4/LWluxQtZiMCjYVNVqBwbgta+Fiy8W36ojVQhU+2hDg/2DuroTDKIdJwDiAg3LctkFBRDURmMYMNiT991+wyumtxOtf+n5NGxYQYIkFlAgBH19bL35ianvdN99zrfDNHKgZi1mIOSwRk1NLmCpNGo6mwlTwEKzZyEee06m2awrLwXIpf00W+uG2Wkj1PVXFs8qzjgDmUfZJrb+d3GZdLy9+93wYdcwo4cORkg4fXF1dVMW56eN2LJFDsNmokO6up1MnaZ8o+AciHHSkyieREJkoytWiEQmk5HDtL5ezs7aWvFx2rRJrv3atWLl/b53ZPka7+WF2O6Wd9/NSe3C8EQicjApgGgYpT2hMgQsSYKf8kb50SKpFM3HwRQmm9SYJlJE8gLE4sKfhTgxAcXRlG8GYrQuPK/35khHOSOoI9F/COUZRMdUoooAotP7ZJho3hl6UwDEUgZx6VLZ05R7onMmBQIs2+i/8SO/OjzJ+A1kTSuAEgz6wZ7TMzU66nS69tDBWNitjur67ICLenzLkuccaa135e1AMVuomp6nQsEGF0W5ds5a7GyZ7m5OhMOubF/lFosXT+7CUAY1C6X/EOQ5lgJEZ9SFvQarYRZioWA7QpvYUu+5A0Rvn30loCoWEybx+utFfqrer2XL3DbkJUvkufX2+gtCRyIUQHRyq5oaBrVWalUPYrEAplk16/FIxgmAuEAjm5XDIxJxE0fl5DXYWyAw7loqLaKfxa9/4bSPF4tB4uXn01Ar1b08siq2fv8Rv32UNzzy0qc5zek/BFlMiUQZ1mOGiHU2Ow51MnA9S/LQ5EHi1Rze/sNiEYysCxCDFGk486RZPV4kIgmFYfgPzZUr7cPS3sCHaGYTZ1CKuJ/V15NfsgJsBnHVqrkBjNiKxQ5DUSQIVTzqQoEjpynfCxDLTFpevVres0sukQR/6VK47DJhiLq65IA1TUlsTj8dLrgAPvbFxVyytt99kGKRlYek6KJpsn5GRuT7dLrkD05MQFYy1t/yMhm0blcK5tu/FgrJoahMdpST6SKfwUKRoaFZzEr9C0Q6DcZY2jfiIpYoP3ftLxUdHZMT2iMFEFViEwrhoP4wtiNGsXoAoiMxNYoVSUzj8cnmEorBU2emikWXnOK775Y9MfZvlv2xxYNfvOyhrsONN8Lf/Z04LDq9TmNjpIkTwCBJglFdslRNmx+DmE5T1bMQczlblW5YduOIRLSrcqVMKYsYCEzGJcWiFOoWEkBUDKJhMJlBrCKJqQL5pgkUSySmc2j2m0lmWi40TaSkqpXGe/1XrhTQqVj1IyEvVeHdR6NRQNfJ1TS7bQsA+fwJgHgiFk4oKZui0QcH3cpK/7YhIp6B3hsS+9FWr5r28WIxaGwNUXfBBkATBhHYnl2G8YIL4ZZb/Hfo6fFN8n6Sjb7+w6amyuc++Z5HQ5hwCFAMIjCyac/sH2gBRzZrj7YIQXrcIFBwr1UTw2hrZ58hKhbR27uUyYhxIjU1kJBk5U4u99+xtpbt7/0S+bibzJziz5kqjthJnTJnk+pnEA3DNcfQimUYxJJQPwqFhBW6+GJJMBcvlp7D7m5hDk8+uaS/7OqrfY+z8pGfOt/X1Iis5e1vLzNjqd99Yy00uxFZKkXzBYjBoByK6rBVRjVBDJ/BArhGAQsxUikwJmQ2p2KeYi0LxP/eDk0TQwYVZ54pn5kjEapYFAziHBSOGVYV9SA6LLaHuZhOYqppLshuanJvps4jL0A0urpZVu+iv6Kl89h3nyEQ8EtVFUC0LBk18/DDMiHohhs8vXGjo/TTRou9RkYsVz6jaXNjEE3T7qVtaalagJhOA5aFaViORBggtryt4sco14dYWh8wzYXHIE4rMa0Ssy9wCQnpBS4413EuDCJMb1QzVZimKyfu7vafo7oO69e7ZMnRAIiFgqdoVFtLDs8HLZ9jfJyqkX0fqTgBEBdoqEqa2gy9JF++Z5CAp7px2nnxGSmhaFRo+VBbE9GWWodBzBFhd2YxvOENUjZNp+Gzn5WMePdu5/5PsRESDQ5AbGz0aLZnEdEohGMBLHAAYvLp/bN/oAUcXgYx3TdGwGOU0FqTmROyVgmFl0FMJj3Jp21W80deSBr7tG1rg/vu41ltreNbo2m2NHUOETtpCXFHYhqAfI6BA9VpMuQMWTfxMYgxMmUZxCVLys/eamuTw2vZMvl+Urz+9b5/dj7wU8Ka63uezcLTT7tsHkBXl8WK5OP+x1nkumnMZwYiSHU+EnH/pnfUhWNUU1j4sxDT4wZGKuuTmMZaZ0HjPE/xgQ/I1vrhD8O//duRe9z/z96bR8mS3XednxtbrrXXW/t1t3qTZLXsbkmt1ZIthIVtgdEYI7DBkoXHh5ljGGZsMIthwIYzHA9w4IzXAQbGC5zBMwePMcYLmGGRMZLcsrqlttSyulvqRb28rdbMjIwbEXf+uHFjycp6/WrNmxXxPae662VVZkZlRNx7v/f7/X1/ZmHjeeQXZ77rPUcWU1ODKNJCuWgxvqUP/oMfhA9/WFvSzKIxD5MpqYs3NwRv+7pioRfj8eTHru1xvxiCePWq/jKIIvg7fyd7j+1txrRoE6KAnUR/5kZBPAhBNFOAsZjOs4I4GoEKx3nPVwBcl2D19u/FaXWI5aAa0OuNXOWxBBWC6HnsttZLvRB1X44XX7SfWAwyE1eSKJy4mJ/ahEdSEG9Fqkwv4STRX88/r9Xofn96+7R2WwfY3HffyVhMo6hEEBcW2Gah2HAbR7kNt05oCKKlKCuI5SaiaQqbr1TbCzz8ba/eEEYIvXMtBKw+sIZcLBacv81b9Tc/8iP6zvzBHyxGDGCLRb689BB4fiWg5qD1h5DZg/o+FQXxCxbLFIdAuQZx+Mp2JcF07fwBPbkZzOKjvKjZ3NT9gs6fR0uMvT5jWnyM9+jc99/6LZKH3sznP1/q9+foEJXDoN336Pb0RkSSnbtrX5iTVcwE8rqZCYvpfgTRcbQ1dxJTSWEZDz5YSSVxEsk96dOVX/n5n68+5ZHLL/GB7UJp1CEyhR/uqAriJEE0FlMotbqI7e+FOPzKBgluYTH1AzqLtx/HflpwHPgDfwC+5Vuq6tZR4Xn6tV0XREevmn2krkOeI4KYpyfGtxdSA/pvvu++6qaNGSOTpFCkpIQHv7Posp3g8vmrK/SvPkMZhox9/vN7a99eegkefRRUFgrwZe4hICJ2il80tu3bxasriPHcEMThEJLdEVAoiK3WwcoYpimIk6ffuGhsIoiVGkRgt3+RBXaLHnpSIqX992FOEKOkYq3s9L1DbWibzyVJKkvJHE88Af/lv8Bv/iZ87GP6y6iH9923/7Vj8hOOs858P4J4nXO174XYEERLUVYQn3mm8HIPX9rCHRV5u6tigzs+9K7bes0LF/TEurYmiFfOkdz/OhCC/4vvYJtsEJhyN3/m3m+F1+tFc5IUO3mHURC7XQgWWxUFcePpjVs/ac5QIYjXhtUWF1cON7uZHmq+X0xGptTw/e9H16h9zdfA3Xfz6+/6m/DJT8K99/Lss7qcrezdn7Y7d7vH0FvRo2ma9Re79sx8jphxnNVcJHqX1ygX+1lMYfrDt/VZTtpMX/wYlKxYk/bSt179N3wDv1HsXq6s5jKJqf09Clx3usUUyr0Q9XuX1RTbMHxxgxgXjII46xYXM4Dv63Ghs6KZZ5eh7v1XqkG0Xb0wTdYFRUhNy40PPFAZgjgYVO2jl998idaK/qHE50Uu88Q//M3Kczc29POefXYyLErjK1+B393QLo1P8HYCxqQZQTT20oMsWs2aOyeIUxTEjTmZFkcjSIZhlmCabbR1DraCN/dtWUHsdIpLIEn0975/8NyDk0RFQYQ82Xbe6hDN527uQ4PO5cNLdfsF1ezs6HFJCP35ua7e6BJCr1GPUx28HexPEJsk04YgWgqzk9Zu60J5A//5p6vtLe7aRCzdniHb1DzdeaceaOX6ZXj4TewGq/wM37X3Caur8BM/wWP/wz/JG7LHcTH5HuZG7nSgtdylrCBuvnB2GswY24RpczHcHBcLfWD9/kOwavTgacIZzAQZx3px8QdM2WGrBffcy372RV0AACAASURBVGOtt3NV6pPz5JP6mMwi8Z57Dr/71ulAb03P5LmC+OJ8ei7G40xBHI6hbFFc7e6783HffXv7172qggh7bKb3ffFX4eMfhy89A8PqhoznwUO/9VMssc27TfppyV561ART2MdimhHEvPdTtrAZjaa8gAVIEhi/spnZ2qgtQcxbXSzrPzxgrD+L7PyZzSqbEYaQjPR9mFtMV3oHbhZpVLwkqYoe29vw5nfqTclrnGOHBf7PXz7Hyy8WC+GNDT1WKjWdIPbaMZ+Jv4qXuUhIm6ecr6owlYME1JR/Pw/LWj03txbTMIRkEFJWENu9g507owqGExULX/u1+mNeX9c2/+NU4I8Dewhiex3F/BHE4VD/DWmc5nOhQBFcOeRuMvsH1TyfdZ65cgXe/W54z3t0m4uv//rjawF0EJQJYt4Xtd/nOuu174XYEERLUR4on3yy+H7396pFQQ+/+/bl/05HkzrTKD2K0IvhtzzCLy5+hOfIaj4cB/7Mn4Hf+z343u/l8SeKiTBJCmJ4mF5s3S4ES52izQWwMfDtlioOAHPesnmB4XZcVRBff27Ks24Pxk5VXgRvbupajXIyolI6cyhN9xLEKe7J20anA72L+noz5+7Gdeayj6VZNCdhhKNNeQB079mf8bXbutbQYHHxNnsxTdhM7+cpfaE8+6xWeh99FK6+Akrx4MUbdJ7UrWc+wK9oNliKWzxq/SHsoyBmN/Nkm4TJBZstkBLkjZ38OjQE0Sb72WnA2CHbq1ohE6BtUbKoI7Ld3jYeQxrq480tiuuHSECjUBHL1tObN+Ftf/IBtljOau8VL46W+Y0f002Ak0T3Mv293ytUDYDv/M6CaIrxmEW2+E98PTv0eXThvYhsp+agATWg36PdLojFsH+eFUqS4RwRRK0gRhUFsd2fwrJvgTJBLCveDz2kg4K+/dv1uGXb/W3GUsfRxy3bC0QEewii7a0uhkOTJJxUAmrEHQevPzSYRhBHI93PUIhbOshPFWWCmNcmLyywzSJdsg3cRkFsYAuM/c1x9KSV9wY6F/PMc9WB96Fvv/0tl25X3wBS6ibr+aTWapE+/BZ+6gP/Gn7oh3TB44//OKytsbNTyaohSQqL22EIYq8HQcsB10cZBZFl+OxnD/5iFsJEtscxoBTDIVWC+PDhR0VzvsqTpLGZlpMSAf7lv4SPflSn8ZUJ4sMPH/rt6XTAP79CgMwX5ulgNDdWqDIMQYxHUSV5r3vfpX2eoVG2md6WemjwF/9i/u29VOuf2N2Bz30OfvuTPPK7P5M//BY+xfkLAryi1umo9YdQLLSUKhQMoyDmBDG2W0GMIohubOeh+rVXEFcLRtRnVw9A2U1vO9EIQ71RI0pptK1zh4spnDZG3rgBX/WWLje72iIqUFzlPJ//B7/GMz/2b9jchKef1kTV2N0WFjRB/Kt/NXuRcUifXWI8fokP8ph4E0mif991D16mZe5BQxAH3flVEDVBNE6MTEE8YC2w42h1UKm9rXWCoHjMNoII+vhyFbHXq/ZCjOZHQYxjIIkLm/AhE0wN+n19XvPXRpdTKKXnTlvU4DJBdJxsXZvd0PnaLUunaWoQG8wc5YCasr10/eYXGSbFwLsUhNz9zW+47dc1CmIc6wnqHe8o/dBx+PjwIR79g3+j0gfhs58tyIWx7hjnz/rt9cGtoNfLJgLfy0nGTVY1Ez4DMC0uTOqdTN3CEux6rL/+EB9aBrMrXh5YDUF83/v2his8/7wWZsvJW5VzfkC0WuCsr9JiRGqWAqMh116KX+2p1iEvyg8nivJfd9c+z9B4+GE9gfg+vO1tB3jDj34U/v2/hz/2x+i3E84zRTEfDnnk0/8o/6eD4o9+sNRD04N3vvMA73kLmIWWUlWLace0z8lqEG1WEKONQa791p0gBh0PAj0w5IuaRJ9DW0m+wXgM6Vgfa24xvXC4QtupSaY34dOfhu6qvjgcUlIcXhyv8Bt/7l9x40//ZT73hP7MjHpoAoXe/nb4yEeAcIwAlthE4vG0vCuvCRfi4BZToyCauXXQmd82F5rgywrBby8dnMlNq0M0sJkgTuuFONkuaKI1sXUwPYFFWsyHRyWI5d6gO5mpwaRi3yKg+NThOPo+VqrU6mJyx6cJqWlgC8wAORgU1gTXhfGjVZXta14XItzbP4Wtll7cms3llZW9PfF+8ierlsEyQd3e1jePUtqrfeiQmgBwC4K4yTLqBcs9GLeJ0aggiOr6dUJKbK7bZW398AVkZvFTrpExE8/iom7sPFk/kxblSPT7R7MoCgGd1Q5tJyGvIVWKa5+1OOpyH+QW00hWi/K/6jW3fF6noz/nH/iB6ammt8T73qcjS195hfv/0Ovz3pUGC+zwWkobJa7LH/1bD/G93wu///fr7jMHUi1vAbPZkKZzajEdp8itYR6WJFDQra/FNAjIT6ohWbbXkRqMx5CMZdauJCOIlw5hT6EYI8ubZVevaqv9+mv60O6wznUEio/xdfw638jP/UKXzc+/BNEY39cLxg9+sHj+d30XfN2FL+jXZ4BHkvckNTjoXOi61dq1QWuVZUosIpZsbRXKi80wCjAUFuH2ysFvxP3qEMuP2Xh/T+uF2DPWRKk/F9vvQeN8KicJdxgdqsVFGWWb6Ve+oj+j1dWDW7JPGnuCano99BonK6+SElTaEMQGs4cZDE3sL+gas89/snp1PvT7DjaJGvk8CPTEMx7rya+ML30JfvVXi3+X22vs7BRFvG95y4HeOkevl9l4XJEHnUQEjJ49OzWIo5FeoIxf2igWa0B3yZvaS+92YZJMy/Vj5Z3Jb/kW+NmfhW/+5iLIJI6LRcjFi3tVxoOi14N2V/9ViUkynWOCGI9VRUHsvvHeV32uOQeHxuIi933rQ1qOfNvb4cJFQPAWPlWxu/Le9yLOrfOhD8Ff+2uHb08yDWa3fl+CGOsmz7YSxOj5V5Cpk48hjqutuLVVEAP2FsTOEUFMI70rmVtML68d6rWmtbowWFsTdC4usfiacyyyQ4zLp3gz/4wP6+v9pZfw5JCv+7pqgKrjwA/d+7N8H/+AHkOW2ASvGqV5ZIupt4RHwhK6lQZSopQi66xhNTRBjKsK4urBGcCtFESbCWKl1UVGELtkE0x2D05r9WATDEEUx2gxheK+2NwsxI7J/pY2YA9BzBqbxpQ2gsZRQxAbzB5G7n+61C7toSs3+MzL1YCTh/7EhPx3GyjXIYL+Pk/BzPBTP6X70gwG8MUv6sd2dvQxra1pJeqwYSftdlbY3XZyBRFg88uWezBuE6PRLXognjtaPrfjZEExveL8TVpXLl3S5W4/8zNadSqVIh3JXmrQ60G751FOob32e/NXhDgcQjKOSFWR2uag8F/36gTxOHCfaV3a7eoAm7e/jUfev1qVgH/wB0/s/c1GRV6DGASwsFAQRIA4tpZcDJ+9SoqLwtXKU9YHsG4E0Wz4+D6lZJZsd0jOh8U0iiCRSUYwMgXxyuHCvMrEq0zalNJj41veInDuusLSm+/F9fV4XGzJKLxnn+LbPrD3AxPPP8cf5l/z03yU9/IfcYPqWH7x4sGO0/OKmjuAQeTD4mLVZhrPRy/EMNQKMBQKYmf94ATxrCiIOtykShBtTxLOe5Emx2cxhUJB3NjQH8XCwtHbNJ0EpiaZLixkoVYZonFDEBvMHqORLmo2tWMLC+B89nF2KGa8/qLLvW89+C5ruQ4R4JVX4Hu+p1rXNhzCX//r8MM/XARZbG8XCU9vfOPhF2KtVlaQ3nFyBQpg4wXLt9huA6bAPieI10fVgJo7j7567XY1QS8TxGl9zu68U6tOf/7P6yS4d70L3vSmvb93mPdvL+lVad7q4svzd+6GQ0g2djJbm54Quz0QwREl1ttEThANOl0e+bn/EZ56SsvAn/2stqSeEPZYTAHW1ooaRAAprVUQBzf0gcU4+vxlxNrGBeRJwixsPI98UM433uZIQUyk2aTJFMQ7Dx+vPy3JVEpdM/z93w9/4S/A8h0LLL3hDuhXpb/XhY/z4P/xfXtfNMvmP881/h4/wJ/9zs38s+/14Bu+4WDHaDb7zPw6HAJra9Uk02g+6hDDrTFJSqEgCkF79eBWmf0URCnJA4GmtSCZNTxPEyFDEG+wVrKY2k8Qk6TUi1SVCWJ48J2PCXQ6VdeSTbWHZZQJ4ppZVmcEMV9eRVGTYtpg9ghDHQVsBsMHH4TnHq+qNA/eMzhomyhA37BlBfHqVb1j8t3fvfd3f/u39f8HA00oL17UE9v99x/cUmNgCGLQ9UhLBsyNl8f2d3R+FZiIbim1DXG4MdED8b7D9UAso9crYrWh6IW4HzxPD8qLi0ce6wG9ADdhD7nF9CV5q6dYiTCEZHM366Gnr7vu4umQQ9Abs+WWGQ8+mNUX3n03fPjDe4uDjxlmEZ2H1ACsrk4QxNhigqhDA1JcvaDJetLVVUEsE8RkjgiiUiAHEakia5OgQAiCi4erQYTi2i4Hx1y5ohOAFxe1Ff+f/TP4E9/p4l9ch17xi9/Ov0D8o38Iv/AL1YM0zdvQx/ln/kKX7/5u+MAH9Nx5mLHVHGeaZmP4+kRQTTwfBHF8Xdt1ISP4QYt25+C19vspiDarhzCdIBZhX8U9aOvyxqTmp6EeU82qrNM/HkZuVMR2u6TOWYYyQWy1smNeWMAlLmym43HTB7HBbGEsX3kSJtoT/dJTVRZw192HO3WTCqJpP/ihD8Gf+3OV3r/58Zg6iAsX4N579XEd1iYghB4oWh0XPZ3ov+OZ8WX7o75eBWYii2NgNGI0FoWCKATrD6zs+9zbhdkVn9bqYhquXi3O9RHrzQF9/Rj7kCngvnbjaNbZWWA0gnhrACUFsbN6eisQIXSE/jvfCe99L/yVv3Jqbw0UGzVpWiwQWFubsJharCBu6MVMgqsJfhYaUjeCaBY2QoDb09evQOl0V8tblUB27W0U/SwFClot2t3DL00M8XrNa3SQ1P3364bc5X6Fi4taSfx7fw/ueOMqbV/xEI/r3qOgbTUvvKBXjD/1U1XW0usRnF/me74HvuM74E/9qcMdpyGwSmUEcW1t7pJMkwTk5oAUpyD4QXAoMhcEem0hJZXWSbYTRN8vEcROh5usoRCaJMYxKD2/2Hof5gRxZIKGsvnwEEFD02DS7l/zmiIbwTaUCSJkx9zv02Jc2EwjWTsF0ULBvt6IIj3QmJ4soCeSrzxX7UZ+6bUHzNXOYAiiURCvXSvsG9/6rbrP2w/9UNFceTQqbpqLF7U1zvePlkLVbsPyiuAV1yNNHFxSHuURPvLCC/rg5hRmIosiEDduMKBbEMROl/ULRydS5Rj3ss10WtPZF1/UsdJxrAfm47B3dDrQXe8Bu/mu8fVoAXXtOuLc4Vt4nCbSNLPU7AwQdIqi/LXTjVZ74AH423/7VN8yh6mBMiEZYQj+2hotSk3IpNQBIimHciucJIbb+r5KcotpPRVE02pBSugu+ewAPrEmznNQg5gkEGcEA7LFaat7pB5pZWXuT/5J/b1pszvZjuJbvgV2dlye+s9tvu6f/iSOmWY3NuAP/SHdI6OkHgJ6IBWC9XV4z3uO5zinKohzQBDjGOTW5Pk7HEEE/dF+6Us6++CRR/S4Y3OLC9BjaRBkZTqOQ9LusRUu0WXIiI6uBQ4CBgOOFFJ3UpCyqiAWG6bHM5heuqRtm0cKdjthTCOIz/R6tIiQZDYN2YTUNJgxDMkot5ro9+Glq9VTdfmNh7PgtNt6IDP1D3FcbeL64IPwj/9xUa9m1MPVVV3L1modvci43c583qVWF5/jDQyfsryb7KvALMTGY2Bnh1GFILYP1TdyEmYBvF+SaRm/+ZvFdfTAAwfv1bXf+wctgecVtU4xHpuPfvHoL35KMDumaker8nkN4sVD+qbnEK5bvYZMkqmAgiRa3OpiuKWPLcHTBL+mFlMobKatrPech9T27zmwmMYxxFu71XYlrdaRFpPdribOo1GxAWKsYZMbm46jSeRf+t8u874fmUhre/zxveQQ4Bu/8fAHV4Ip08gJ4hwqiFJCvD2sKsBB60gEsdPRNXsm9dJ2BdE4vYyV0thM5yWoprCYanaUW0zXjo/N2kwOYR8FsdcjKBPEKGJ3116r8EmgIYiWwUzm5ebm/W7Ki5vVme3Smy8d6vUdRw+0Fy4U1sNnn63+zvIy/N2/qzdQldIT2bveVQRrHFXk63T0V78t84klweXxj1u8krkNmPTZOAYGA4Z0CoIYBMdCEE3/rFslmQLcuAGf/3xxjt/61qO/NxStNtotVQkZuvapKQspSyElRJEiGejrLd8xvTS/6vVBYQiiWUCPRuTV+UX9jL54bCOIaQrhIAYUMV5Wg1hPiykUi5vWiv7jjYI4DwQxSSDeHmlLLEaBah9JQSwHwAyH+mMYj4uxcxJCZI9///fD+9+//wsHgW6C+iM/cviDK6HTKWzeUQRy5fzcEcQ4Brk9qiqIh7SYgv48TH/ZL39Zn7d5IYh5LkOvNxFUoxdzthPEJDRJtFmrkvOLt3ramcIkQTx3Duj1cMpJGZFEKWpVh9gQRMtggk7KDXJbN1/iWlpSDD2Pi687vIzX6Wi7qHmPL3957++4rq7d+ON/HP7IH9EpbUaNOqqCaCbp88vjSquLRx8/vZCQk8BoVKodHQwY0yK33PvHQxBB75AvLBQE8ckn904+v/Vb+jqKIh2IMs2Cehi029kmQ6fapuTa785PH8s4BnlzhzQpJSe6Lt1zlnXvPUFMxuyXCWK1F6J9BDGKQA4kLkmJIDYKYntF7/jnCuKc1CDGO6M8WMcoiEchiFAohYNBEeLV671KDZTj6P5AkwN1q6WJ4dNPw4/+6LExlcl7cLhwYQ9B3LC8g5A+f+GEAhwc6T5cXdULdNPqy3aCaO6/3KGzT1CNrb0QCwWxShA7F48eqjcv8H19+8exvu6Mggj6mlaQE/2GIDaYGUYjvWtmJrJWC248/gKqoBqs98dHkuw7Ha0gGoLx/POFkmCglK4DcBx9LA8+qBdmQXB0H31OENdUhWR86un5HpDCUBM134d0d1hYEwAC3/QiPzJ6vWrT+91d+KVfKhYaOzvw2GP6++EQvvqrj6+00+x6txc80lIJ89UvzkFH5wxxDPLqZnVR2u3S7VlaQX8CcN29NYh7CKKlFlMpIRrGOKTE+BWCaOsi8iRh5oIgI4jzpCBKCdFOUfcqUHjd4Mg1r2WCuJ+9dCouXYJ/9a90Gtv6Onzf9+miuB/90ePbZctQ6Z8HDHrnq20u5kVB3B1PKIiHt5ga3HefvqWvXi2I1VE3DU4KRkHMr699Wl3Yeh/mCmKkFYPcYnrJwoaFJ4iyitjvQ2vVbLglejzN5MU6BdU0BNEyhGEWGFHalXrpM9cqv3N5PZryzNtHp6Nf1wzi4zG8/HL1d15+uZhYu92C1B1Hk1PzWmsToS3P3ujn4TjzhiTRg2wY6olttC0rPRCXl4+vh1O3q6+PckDCk0/C7/yO/v4Tn9DHkyRaabx4sdTb54jILaaLLeKygvicpbPfFEgJ8vpmbpF1SKHbrZX6NNVimu1gTBJE2xY2UkI0SvK0TgcFnmttn7STRm4xXdMet3mqQQxDSAZh1o9Ub4O2Fo9esDRNQbztGux3vUtLV1evwt//+5o0ngBcV8/BOUHsrLPEVq7gICWDQRHSYiPiGOLhOB9LTYrpUclcu120AVIqa41laR3bfgRxsgbRVgUxDPU1mIzjIokW6NxxTDvac4IyQRQC1q90QDi0GOvN/iSBNKlVUE1DEC3DZIuLfh9e/EJ1y+LSHUdTOswiv+ykmaxD/GIpc+T++4uwmuMgiEaBdBd73MkLxQ/GYz71qaO//iwQVtfUDHfTCkE8d+741Cnz+Z07p5s/G/zar+nCftO/cjjU7fRWV6vNao+CXEFc6VQtptexT2raB3EM0Y1tVJkgdupHEPezmNpegyglyHGSuyocEnC9fFyrG8y9HSx3QTiFgpimkCZWE8TRCNKhPkCzMG0tHl0GPrSCWMYJX0yGIJp7cNBeQ0ChImaTic02U03wNUHMycURLaYGV65Mb+tkG1xXz4m9XnYuu122WKJlNtoiu0NqhkNAKeIozWruNDp3Wdq08ISwJ6jmvJMF1YxLQTWyIYgNZgMTv29UKMgUxGeqK7TL9xxttMwVvJKqNFmHWCaIDzxQBKEcJ0FMu33u5ZniB+Mxjz569NefBcwur6nrHA1SvBJBXL14fNufZqEzHOraUNN8Vkr46Z8ujiUI4K67jrc5ralBbHVdUicw5dtcU+tF7JzlkBLkzd1cAdUKYsfKCPKTwq1CauahBjEK05wgChS4rtWLyJNEThBbOm0lVxABZGw1QRzfHJDESUYuUhCC1uLRvYRmIysMD6EgnhI8b0JBDHQdQF6HmG3Q2GwzDbcjkBEpLsKQC//wITVlmMAaIUoJoZbC8/SYurgIdLuVkiDbU0xHI0BKUkTe8gnHoXO+PqnesH+SaZ1bXTQE0SKUW1yYzct+v9qGAuDSVx2NpZnBe3m52L187rlSsfxQ9wgGfRwXL+ox7jjqD6F4DRW0udsr/XFJwqc+LucyRnhSQQxHCpciaWj54vGtXsv1Y0kC3/ZtxYaCef8k0WU0jrM3c+Eo8H391emAcr18IrzGOXjlleN7oxNEFOneaxVbVLdbO4JYVhDDkH0tprYRRClBhmk1+dJza6UAl2EIoucB3S6eURAB4qKXpY0YPXctCzjRFlNaLVrtoyt3QpQ2IlN9rdtmP95jMfV0DX5BECWgrCaIoxd0TYjuR6rtpQhxbJs1Kyvwjnfo/sw2w1xbS0vkF15savQtt5gOh8BgQIxb2JuDFp1uvewY+yWZNgpiAytgdnrLCab9Prx0s6o+XX7oaJKQWRwuLhaF36ORLrkAXX5hFo5XrhQ3zHEFneQTtxKsr6WFpQ3YeCXiS186nvc5TRjVbjwG0pQwEhWL6cqV403ILFuoLl7USmIZjqMJ4vLy8dlLDTqdjFw4RZLpNc6hXp4Pgjga6f8k2YQoANrtWhEM09y5oiAuL4PjFAQxSUCl1ilQUkI0TqsWYbe+BNEsTn0f6HQQgJMn79lJ8g3GX7lesiemx5JgalC2lNqmHsJei+kwDqDXKyWZ6jhzmy2mo6/og0syBdGsso9TzW+1OHJo0UnD3IPLy+xLEG1VEMMQGA5J8DTJB2i1ajeeNgriXlh+29ULZQXRoOeMeHFUZmaCS2+6eOT3MvU6F0svZeoQn3qqeOz++4/XXgrFxJ2mEK+c52EeK344p3WI5txFETAcEtIuCKLnsbTq7vvcw8CQbDPpvOMdmhAamBS447SXGvR6ekL0XJErFRKfm89MachoIQa7KSRxdUJ0vVoriKMRehW2slLZsEHG1pELKXXieFJWEGtMEMsbQH5Pr3JcshpNy4Nqxi/eyBXE42pxYVAmiAeuPzwF7LGYDoD19bnqhTh6SY/5ZrONQJ+8ut2L5h4sK4gRGeOwnCCaDdMYr7CYto7HJjxPmCSIS0tkBLEIYSKKmhTTBrNBhWRkEF95gSHFyrXVcVg5d3SvjBnAywTiy18u2lsYnARBLFt/wqULPEKp8HAczmUdojl3YQgMBoS08YzFNAiOTX01KCuIoMn+hz6kQ2u+9mvhzjv1YydBELtd/dr9TlIJqvnsE/NhSRnd0KvlfFHjuCBErRY1k20ucgKxujpBEKV1BDGKIIoUqlxD6nq1W9AYmMVpHEN/UX8mRZKpHoNsJYjhy0W7GYcUWu1aKYiVNhcDYG1tgiDGdhPEl7dIs+pDE1DjOEXJQ11gFMSFBfIFzphsQMr659lKEIdDUINB0VMWoHX0ViXzBjPuGDeY54HT7xIQoUyFbSSbPogNZoNRta8qADtfrPafuLwyPJZwNTN5LpVaDz77rM4ZMQNZv69/LqW+eY5rAV1WEMOFc7yFkmQ4HvP449XPYB4wHmeR3zEwGDCmVQy2fnBs5NpgUkEEfX4+8AHd91CIk7GXQnH+VhdlhSB++osWrsKmYHhTM57Y2KK8+jVZNyE1SumvnASurRUWU4BYWkcu5CgmSt0qsXCdWp2/MsyCXCloZy0i5qUX4vjqZmZPPFkF0VaCWEkxnUcF8fpuNo6qTEHUylPd0oQrBLHVAsdlZAhiliZsI0E0wYjpQI/5xlHjd3zranZPGoYQm7lQCGgtd2ihGaN2ZDQKYoMZwVyY5b5H21+qNga8dOF4ElzKwSVmQh4M4OMfLx4/CfUQCgUKYNhd4y6eY40b+oHxmPEYnnji+N7vpKGUPmd5e5Ks4DufIwP/2AliWUGcDPW5lrXNPAn1EApyurqiKgTxd56fj1js0Ya+0RKzY5ptd9fJYgoFIVZKX7tKsZcgWqgghhu6XibF0bVrjgPUSwGehNkI6izrwTxXEGO7CWJ4dScn+gIF7eMjiK2WvqfbbTs3fzyvquIPBqBWJxVEuwlieL0I+9IhNfWrXYOCIHa74Pk6ISnGz69tImllSI3Z1FZ5qxl9MXYWa8YO0WOo45Q2+oHWSpcgI4gpDkRNDWKDGWE00pNFWT3beLZ6NV6+63hu3HZbh9SkaR5eCFSJ2XG3tyi/tyGIo7b2XuY201DfjPNUhxhFenEtZfZ3DYdFgTqAf/wW03LIyI0bxeNxXMSiH2d6aRmGnC5P1FW+sLXA9etTnmARlILRpr7GJH5uT4T6EUST7JimxSbHXoJoV5uENIVwIwQUSVYzI7LVWR0XpgaGILZX9YcwLwpidHMna5GQKVDHqCAKAW9+MzzyiJ2Kluvq82aOLY4hWr04NwQxTXWbEmU2akhrWbsGxf2XJFm3oG4XvxJuIq1UEONYDxGmF6mTE8Tja8s1TzDXrhFpWqs6pAYMQWxSTBvMAFLqwSWOi8SubhdeeT6q/N6lB46v2t4oTNPsN44D99wDW1v638dJED2v2HGLe8vEeAVBHM8fpvkwMAAAIABJREFUQZwMF1K7AyJKq5zAr1h5jwumj+UTT+iv0UiTRaX0+QpOaIw3BNHpdbiDUu9DGfHpT5/Mex4XkgSi3TGClBhf73q79bOYwj5BNZbXIEoJ0dYIl7SomamhRXgSZoHaWta7HHNTg3hzmLsQjttiCtW5xja4bmZjK/29w4ULc0MQpQQ5iKrtZny/lgTRKwWWFgRRVpJMbSWIUQRqZAiingw6KzU8iey1mbbPLVQVxCbFtMEsYC7Icr+qfh9evFqd3S6/cY3jgiGI3e7ePllXruhFhZQnY9Exr5cuLBLSLuoQM4L4hS/YG80+CbPbZJTfaCcs0sCATtc51kWPwQMP6ORS14Xr1+GTn4RnntE/Oyl7KZRqe9pt7uS54geR/QQxjkHuRggUCU5egzi5UKsDJoNqwpCpNYg23YdSgtwJcUgKglhTgl9GThDX9W7fPCiIameXaBRPWBSD2tyHZYKY1yH2L9BhpMkygJS6b2u8/+vMCnEMclwElZnNtjoTxDiuEsRyewQbCaKU2TkM9TjhZMnrnbWa2WkymLHHzHmttT4+EoHSNYiRJAyVlffjSeBUCKIQoi2E+KQQ4nEhxO8KIX44e/yfCyG+IIR4QgjxT4UQfvb4e4UQW0KIx7Kvv156rW/KnvOUEOIvn8bxnwbMBF4hiN2Ul7aqq55Lb750bO9pbKZLS9XWGqDJh2l7ceXKsb1l5b0B0laX0F9khU3OcxWSGJIYpbB253QSZjAxBDHciSs9EJdXTsbf5Dhw113w9rfDpUslmyAnZy8FvRB3HKDV4g5RClFKEj79qN0jp5Qgh7K66521SLDRhnaSMEE1lSTTKRZT2whitDPOFES/QhDruDA1yAni2gIwHzWI6XMvEJdrSQMPECfmfLARJqgmr0PsnkMAXTI2YfH5i2OQYaoXzmQW05oSxHKSsCGIHnFFQRyP966zZo04Brmxm29nCxR4Pu1ezWJoM+yxmC4EiKBFQJRthCiQcW1UxNNSEMfA+5RSDwEPA98khHgH8M+B1wNfDXSA7yk952NKqYezr78JIIRwgZ8Avhl4A/AdQog3nNLfcKKY1gOxE97klbQkBXkeF1+7eKzve+6cXuwvTrzs+fOwva0HvkvHx0lz5AqiEowv3g3AMlnBY3Z3bs5HW7097UnC3QmCuH6yHqcggNe9TtfanDsHr3nNyaph7XZGEIVgeSHBpyiaffl5yUsvndx7HxVxDNFA7mmRUEf1aTJF8bnngNXVPSE1Ni1OpdQWYYcEafpYuk0NolEwgrX5URDjZ7+SEcSMYGSr7DoRDM/T122uIHb0zl5OECN7e+jFMchIlRRE3TKoTufPYJrFtLLfaGkvxDgGeXM360VKXgdc17F00mLaagH9Pi3GRSBfjXohngpBVBqme4iffSml1K9kP1PAJ4FX06reBjyllHpGKRUB/wL44Ikd+CnCTOBl6Tr+ytV8dw5gbSE69oW/sSL2esUu5sJCcYPcccfJ9DQyN6JSEF7QBHGJrOBxzgii2W0y/w+HaYUgrlw8nS3xfh8efFATxJNEWW2LFtZ4kN8tfhhJq22mUoIcyeqixnNrF1AD+r6+887ivv/4x2G8sF6tQbTMYhpFuu7JJSkUxKYGMVcw/DVd7FytQVRWEsTkOU0Q882aQK+y62Ixhb0270FLJ8bl92BsJ7EAQxDT6mabV2+CWFYQAQRos7DFBDHaHFTnw1ZQ27F0j8W0BfR6JQURkFFteiGeWg2iEMIVQjwGXAX+nVLqE6Wf+cCHgV8rPeWdmSX1V4UQD2aP3QE8X/qdF7LHpr3fnxZCPCqEePSayf23GJM2RYDhCzcqv3N5vRpYcxwwNtMrVwqCc999sLGhJ687pn66R0euIKYQnrsT2KsgmoAc2zHZniQMFR4F01+6eLbYR24xBYbdc7yJEiO0PKgmjrXFtBKt73q1JIiep4OoTIBSGMInXrzT6jYXWkGUeUiNaGoQgYIgBv0A/CBXEBUKYruSaA3il64R4xchNS39R9TaYurrNLhcQbSUWADIcaoTMMtjqePW8j4sE8ROp2g345JUlHzbzmMcg9waluqAGwURShbTjCC2GOcbIUSyURCPG0qpRCn1MFolfJsQ4o2lH/8k8J+VUh/L/v07wN2ZJfXHgF/MHp9WJTS1MaBS6h8ppR5RSj1y7iQTO44J0wji7ovVq/DS5ZM5XefO6dTL978fPvjBQoG6fPlkGq1D0VIgTWG8rlloThDD+SKIZjAZjdAL6tgrFEQhWLl0trZUKwSxs1oliFlQzWRvRlsQxyBHSXXH1K3nosZ19Xl861uLx/7r0+cqAUs21iDKoSxZTIs2JXVULgzK43TQ9fCQKESWvGcnQUy2db/YnGBkSnCdFMQ9FlNX13rkCqKU2KoAx7shskTwHQEIUcv70Iylaaq/1i7qGzIPqrGZIG6PqhumNe1lCXrsEUKv6dK0TBCLtN469UI89RRTpdQm8B+BbwIQQvwN4Bzw/aXf2TaWVKXUrwC+EGIdrRjeWXq5K8CLp3PkJwel9qpQADsvVTurXr73ZEZew59braL3oRAnE05jUFEQ1zRBnEeLqWmqmqaZPXg4JCLI+wnh+yyvnq2w4FarIIhhsMTreJJWFgVNFHHjBrzwwuyO71aQEqIwqdZc1NhiCnD//eR9Okeix+d4sPglKRmN7GH7hiBOC6mp66IGCoIopbaaGweDCaqxkWAku6OMYGSBUa4eVOpEMCYVxGHSgk6nUBCVgiSx8vzF20Mi/EJ9cvV5rNP5K8NsbIzHsHZZ/yPvhZi1mxkM9nv2bCAlyO2wqUHMIEThYBiPyxbTccVi2hDEY4QQ4pwQYjn7vgN8A/CkEOJ7gG8EvkMplZZ+/6IQuspJCPG27DhvAL8NPCCEuEcIEQDfDvzSafwNJwnTaN33q2ECG9dk5fcuvf4EmulR2EyTRPfTA7h48WR3cisEcfkiMJ8hNXvChQaDPJUPAD841h6SNsBxSim0vT4xPl/DZ/QDWajC7/zOjA7uVaCT95KqpabGCqLBe96TfeP7POq8vdgtVSnjUWqNIhxFEA11CJRs2lzkKKco9hZd/DJBtCxoyCDeGZHg5fei8PRypM4W050dYH29IIhgbw+9nVGeQgsgGoIIZATxih6MfGJi/DxcwrbzaEK/GoJYoGwzrSqIhcW0IYjHi0vAfxBCfAZN8v6dUuqXgf8duAD814l2Fn8UeEII8Tjwo8C3Z1k2MfBngV8HPg/830qp3518s3lD3pSzTaX49eZG1VF7+eHzJ3YMRkU0x3Lnnfv/7nHAKDZKQbio/655VBCN4puHCw0G+YIHgMA/cwQRSgS/3WNEp7CZZlGuttYh6r5P6Z4axDpOiIYgJgk89JC2mSMEo+4aIaVVnkV1iFJCNEqyPojVkJq6LkxhQkFccvGyZGGtIFpqMR2MiHHz2h6RKYh1s5j2eoXFdHMT1OpaNSjKVoK4PUQSFBbTmhPEsvK0drdJE46QeNaGDY1GwHBEjINjIhFrHFID1SRTk2JaVwXxZPP3MyilPgO8acrjU99fKfXjwI/v87NfAX7lWA9wxjCLL88rFEQhx7wyLimGQnDp4QsndgznzsHTTxffn7TlrlKDuKjZaaEg6g9kHmoQzbkrE8QI/0wriFAi+N3pBPGxx/Six7begrq5s2osplQJouvCu98Nv/zLQKfD7m6fNqFuIyElYdi2YtEgpVaAXdJGQSzBcfTHkCTQWQ5yBTG1WkGcqGHz6plimrcNQg+f4erluVAQo+2wajF16mcRLsNct1FkCKIoahCTBFTKYGBXuYkmiEMSlouymBrXIEI1ybTdJlcQi5Ca+hBEu67WmsKQjLKNy9++yYhi1Rq0XVbPnVzz0nZb1yEJAXfffWJvk6NMEMPWEnheQRDjGNJkLgiiURDzcKHBgDGlFU7g5/VdZwn5+Wt3CWnzAF+kxwCkJohbW/ClL83wAPdBFEEUqT0W0zoSRJO8Z+zRDz+cJZp2OghgB9103ZagGqUyBXGcIkiyFNOmD6KBOZ/t5U5VQbQ1pGZgLKYmpKZ+CqLr6jm31yse21q4skdBtPH8jbfHgCDBRaDykKG6E8TxGL1Wa7XwkcT4OknRQiV/uBVBrFO9dTiZgMCv9Vg63WI6LoXUNCmmDU4Re1QoQF7bqPzOpZXRiasxDz4Ib3+7Djk4aVQIYuTA5cuFxRQgHM+FxXQyfVZNEkQ/yNsInCXk56/TZUQHl5SHeDxXEMHOOsQwBGRMnDVZF9BYTDOC6HlaRaTbxSNmm6VsYWPHAtXcY2kUo3BRiFwB9ryCINUVxmbaWulUaxAtOX+TiHfHSLxcza9riilU59zN/pU9CqKN52+4qXdHU5ymHylVghgEsNhLCJAoyINqbAqpUQrCm/rCqtr1RW3PIUyxmDZ9EBvMEtMIYnStukVx+ULKScPzTm/3z+yYpmn29995J312C5vDeMxoVOEbVmJSQRxvR8XfAPQWxIm1CpklzASigjYjoU/mm/i0voizvKnHHpvV0e0P04qk2DGltgqiIYjlcedNbwK318JDZvVhwpoaRHOPxeNY1/VQ75ChSeQEcbVXUhDtbXMRDmIUTnYvKpwaEkRzD5bHn632hbmwmI629OSc4Or70GkURCjWBEsLaX4fpjgQ23Ue4xiinRAn6ylbbhlU5/G0bDFttYB2m5aQBUFMErZvxvs+/yzhlnuuQoifY58+g2UopT5ybEdUQ0wjiKPrA8r8/dJdZ4tlLOp2TyilC/Pjy3fh8V9YYosNVvJRdmurCNCxEZPtScJdWfRABJZXzuYeTE7wcRgtX4INeCNZBG4UQavNi5Y1oMk3I2RU7JgCePUkGJMKIuhNoqAf5G0SbCSI6VgH1AD5oqaO528ShiD6qwvVGsQ4YjSyryY4HOj7LzUEw20IIsBW67z1ITVKQbgbA6pk9a7f+StjkiAG/YCgQhBjq85jHIPcGWcE0df15k09d8Vi6ro6fCnoeqiBIEXgoNi5EXFKES4zxautXp8Cns6+toD/BnDR/Qgd4IPAHBgB7YVSxYBSVssGN6srsjte2+MsYXkZFhaykJoxPNV7SD8+R60uzLkzjVUBwt2kShDPnc1BpKwAj7I2JblFOGt1YZOdBrId00GEq+KCIAoBjlPLCXEaQQTw+p0JgmhHDaKUWVs4qXe8oVAQ66palJE7FRYWcUhxSHXdjIxRyj43hlksx4Yg1lBBNBbTcg3ipmd/mwspIR5EE+pTvcmFSTE1bcuChQA/I4hmHLVpToxjkLvjLBHa046amtuEQV/Gvl/0tm61IOjrwTUrSmFnQ1rT+ukkccvVq1Lqh833QohfB/6gUupjpcfeDfzPJ3d4Zx9S6gvR96tkaHuzaim99ODqKR/ZycJx4N574fHH9WD6OfEGXs9egmhzUE2+UxgUZCgcplWCeOFsrlzNgkYpCBd0m5Ie2YeQrURtmgwhS8DMLDUyq0HUixpRS4vpZEhN/vhCBy9TMBTCmhq2KNLn0Il0emJeQ1pTBXgSOUHMip59Yk0Qs4j90cgi8pWmhGGKQl9jAgWOixCcSUv+fjCbNOXrd8tZsT6kJo4hGsqpBLGumzWmyboOQoPWYjsniMZiatN5jGOQGcmXeAREjcU0Q6ul5xpjMx31O7ivJKQ4uKQkoXbVnPXP6SD+t3cAH5947BPAO4/vcOqHcg/EfEGtFFuDKne//OaLp3tgp4D779f/T1P4wvg1xLilXoj6g7FZQTTnLgiy3XClCEMqBHHljrM5gphQhTSFUUYQ813vLMl0OMSqXbaypUbi6x3TbEKsI0F0ipKKynnyFtq5RdE2i6kcSRwlkQSFRdhxTiVYy3YYYiUWdfqsh9TR7FKfS5sWpwyHhLSLNGGh/a9BYJcN9qQxzWK6qZYmFET7akjjGOQozgli2WJaV4IIE0E1Sx189Fxo7kObNk2lhGi3bDFtFESD6UmmpaCaSNai1cVBCOKngb8thOgAZP//XwALoyjmB2WCaJKR1GDIplosfsn1uPjaxb1PnnOcP6+JRprCuLPC09xXIojzoyCmabbAHo9JlTEhAI7D8nlbtuyPF+UU2lFvHQCPhBbjXEFUCiuIhYEmiCEOCdJMiNmipo4EUYhigZqWDAveYm+PxdSGBaqUILdDXNKs16gh+KJi0asrDEFUWYG3T5wrwKCsOIc5dncZ0SHNWiQ4jh41jU2vLjAqfqtVbNgMRD8nFoCVFtM4hngoccvkoiGIUwiiURCFlTWI0UAiUJnNW5N8o4TWGdOSTHWri3r1QjwIQfwo8LXAlhDiFXRN4ruBJqDmCJhGEMMbA70jl2GhI+2xBh0jfB/uuSdbnC4u8jneMFc1iObc5Yvr4bAYQAD8gOXlUz+sU4FRbJSCUaewP3cZVoqdbN0xlc2OKTA9ydRf6ubpe7YpiNF2iEtSKIjZ+WsUxBJBDNrg+XhIvTBVCpLEOoI4pp33QDQEsW7korxBs5C1HaXbzWtsgYwgWmTFICMXoxhBWtSQuvpc1u0cllEhiCu9PRZT2wiiHMq8rs5suLXb9VLxp2FPkmmvR8A4/6yQUS16Id42QVRKfVkp9S7gfuAPA/crpd6llPrySR1cHTCNII5uDCs2xdUFOeWZ8w/XhbvvztS3fp8nxRtYILvrpIQ0mQuCaNIVGQxIyhN74NeCIA5aK/njPQbWEkRTc1HsejepbVOTTJcmFMTYDoIYRYUCHBmCmP0BjYJYqFFKAZ1OriAqsEYFNlA7u4S0coupyFYiZ3Ej9FZwXb0YT1OKfrm+z9gtXdAqZbhz8m2uDgIpQYZJvo1txlLfL5TQOsIob5ME0dqQmlGcN4BvWgYVKFtM222mWEzr0QvxwLeyUuo54JPAC0IIRwhR4+Hg6DALL8cp1tXjrVGll97asl2Tw3HB82BtLds5dRzGvVUGlCbGKJoLi2muvgwGRJS8GWdYQex0ioXAKFjMFwqaIBYbGjbtmEqpCSLZrrfZMRWiIYgVgrjc39PmYjSc/RgkJUQ748xiWiWIjYJYKIhxDH7Xr6rAlgVkJFu7SAIUINBR8lBPa5u5B3MFUQhGixcrLqI4jCsq/6wRxyDDVNfWUVWf6gyzwRFFuh+pS4JA5QpiHJc2lGcMTRAT3QoHckdGXefCMvZYTPt9gorFVDYKYhlCiMtCiP9XCHEDiAFZ+mpwSEzrgchgSFnhXzt3NvV+39e7p/femz3Q73ONUtPDKJo7BTGiFMEX+Kys7HnamYDnFYqFDBZyS5TNFlNtqYnzRU15QqyrpWZakqnX9vCzRaux1ISb41M+sr0oLMJJnmJqQoYaglgQRCk1QfQtbFVikGzrsdJcX8ZiWjcFEaYQRGC7f7kaVGMZwY9jkOMkXzAbi2ndyUXFYrq+iAACMhtntsizZdM0jiEK48Lm3fSUzTHNYlpREGVTgziJfwhEwO8HdoE3A78E/PcncFy1wTSCmAyqM8HqhbO5rWqCQe68M3ug3+cVLuaWh3lREHM+NBgwojS6BkFhGzpjKKtuaa+f/919dvMUU7CPIEYDWSQnNhPidAXRA6+lf5ATxI3Zrk6VyhTg3aiqIHqNxdTAdbWqn6bg9lq5gmhlxP7WAIlfbNZ49axBhGKTprzJsdm5tCfJ1BZiAWUFsWpPrOP5K6NCENc04/eR+h6UdhFEnQqdNgriFASBHkvjOLs/8xrEJsV0P7wL+G6l1GOAUko9Dvy3wJ8/kSOrAaKo6IFYnrzj3epu/dqdZzNi0RDEfh9txez3EShCslnGYgVRSr2o9rxSWM3ugDGlLXD/7BJEKBHEVjcniF2GWUdzbUm0ZTIEMyHGEwTRrTW5mBZS43laRYSygjhb+cm04kjCCIFCEmS2toYglmFURLc32arErhrEZGeIxM935EVjMa1cw1vtC9VeiDYGnESqOH9ZDWJDEPX/jYIImiCqbJMG7JkTtQqc5gqiOYcNQdQw17IQlFJMSyE1m7MvuzhpHIQgJoBZRmwKIc4BA+COYz+qmmBaQA1AOKi6dldfs8BZhCGIoxG84Q1Av0+LcVGHmEUJpxbeh0Y9bLWKczfeiSrhQguLIt8dPoswE4lyXEbLl4GsBhFy361tCqIuyi9NiJ5byxYXBvsqiDlB1J/VaCuafOqpwqj06UjfeFEerd9YTMswBNHpda2uQYy3h1kNYqZAZQmYdbaYVhRE/9yExdQuBVFKkFE6YTFtCKLr6vEzTUEs9EE4mYKYJRGliTVzoiGI6YSjpu7n0KAyFrkugU8lpX73mkUD6gnhIATxE8AHsu9/Hfh54BeAR4/7oOqCMkEsDxrjiUCI1XvPZiFbq6UH1CiC170O6PdxSEnwdHl+JFEKK4uBp7Yn2YkrBHF59WznN5V7IYZrep8oJ4jZit6WyRCK5L1JBbHOO6bTCKLvg9fRTMPsmIbbsyWIps43DfVxNCmm01EQxEJBNPY2mwhisjMkwi8SFBuLaeUa3vZWaZcVRMvOn1YQacbSKTAquHAEtNuFgghWEX0pIYrURA1icw4NKgoi0Oq4FYK4/YpFN+QJ4SAr2A8D/yn7/n8C/gPwBPAnjvug6oKpCmKaMhy7ld9be+3a6R7YKaFcx7a8DMvn9cjqE2nLYqTVAhvrEKcSxEFSSZ9dOX+G5UOKATRNYbSiFcR81zuyU0GMxumeGsQ6k4t9FcSuZhq5xXR3tllkhiAmof5msgaxURA1DEEUliuIye6oWoOYKYh1tpgKUZDEtN3Nk4QB685fHKXIhKobw3FrqQBPwnwGRbuZqGRNtIcghiEgYxI8HDPSNzWIOcz6RmVhwq2eVxB9YOfG7IPbThoH6YO4qZS6mX0/Ukr9LaXUX1JKvXRyh3e2MY1kqN0BA0qeN99n7fLZHXXNhDgcwhu+Ws+UbcYZQdRqgY11iMZi6rpF/eh4lFYUxKXzZ3s73CiISsFo+SJQtpjqc2fLZKhUEaxgElcNwaizxXRqiqkHXkev1HOCOJitzzsniCN9XcmmzcVUmPPp9LsTNYiWEYwdTRDz+ievsZgmSakXYrtdaXNhE7EAiHdDJEGhPglAiFoqwJPYSxDtUxDjWK9hhBwT4+l6bmgUxBIq5xEIen5FQdy5aVHfmRPCQdpc+EKIHxZCfEkIEQohnsn+XcM9v+PBNIIYb+7mCgdA0HLO9ALW/G3DIVx8QNdatgn1Z5CpUDYSxDyYxqyb0xQ1Hlfak6zccYZPHKXd7hRGCxf0Y5ZaTE3ISSwT4qwVSVOUfwsFsadnR0MQZ90HsVAQYxQg8xpE/Qec5THyICgUxF5VQbTMoqgVxKCUYlpfgljepMkJYqeT33uAVSE1SoHcCZF4hRsjCxmq81hqYK7hNAXRaeU1iAqsOY+mH6MjI2IzlgJ49U71LmOPgrgQFEowsLNlYTjGMeMgFtO/A3wD8N8BD6HbW7wP+F9P4LhqgWkEcXh9WFGh1hbGZ7pHW5kgdu/UVtoWoS6cziymNhJEoyDm6Y+jUXVC9zyW18+2xbRcg/hE9Fo2WbKWIMaxXoCJSCLxEaiMIDYWU9ibYur3JwniaR9ZFYYgxuM4syaSn792u/g76o6yxbRSg2ibRXE3JCopiHUmiOV7cHk5e3CSIFpE8ONYt+ISQIKnx9LM6t0oiMU1HEXgdwP8crsZGVsxJ5qAGifRCqLTKIh7MEkQg4VWRUEc7CorAxSPEwchiB8C/rBS6t8qpb6glPq3wLcCf+xkDu3swxDEICgI4mgjrBDE1eWzfQWWCWLv4oJe8BHqhUOaQhJbWYNoUhXNwpXBoKL84gfFZH9Gcf/9um5GKdhw1/mnfDchpdkReyymuiAfnDjK1Kds+eXW22J6uwrieFxMlLNAuQZRok0rxiLc2EsLGILIZA2iRQQDQO6GWRiZgwBElkZbZ4I4aTFNy/OJJdZEyMjFzhiHNCMXKv8jGoJY1NGOx9Dq+zlBNLXANpzHOIZoJ8QlRRqC6LiAaAhihvJYpBSIfq9aFyyjSveBs4iDEMT9dKwzrG+dHEwPRM8r1A3QtRlOqfZgde1sJ2F2OppkhCF0urpKP7eYAkSRdQRRqUJBNP9nMEBSUgwDn5WzGT6bY20Nft/vK/oEbbPIr/ABxgS5PdiWATS31ERhYU+E2tcg7kcQRa+LS1KoGLEsrvUZoFAQE6IyQax5H8tJ5Apiv1etQbRMQRzt6AsuwUGQ5gpUHQli2WJaVhArG44Wnb841gS/IIhpQxBLKCuIwUJAMKEg2kIQ5bY5h3rD1AR+NQRRQwh9Lp1Mq6DXI0AWq/MosjJh/zhxEPbx/wD/WgjxjUKIrxJCfBPwi9njDQ6I/XogJsNqQ+q1iz5nGY6jPwOlMqLR79NmTIqbtbqIrLOYSqmP1/dLCtlgwJjS6sYPit3gMwrXhbvugm/+ZghWNMtKcHmZi/n1bcNkCFmLCwmODDOLqbHUeLUmiNNCanwf6HTwiAuCKGV+TmcBKbNjHEdEBFp1yiymjYJYIFcQW208oU+qytwYo91k/yeeMoZZ6FGKWyEYdSSIZYtpuQZRUpr7LSEWYAhihENSBJw0BDGHuYbHY21LrCqIdpxHKXUdqZMpiM05nI52W69LXZesT3dY2Ewjad3a9LhxEIL4F4HfAH4C+BTwY+hWFz9wAsd15lEmiDs7xePpoLpNuHbn2V+9mgV6FEFrqUWLEEW242YhQTRKSqtVIveTBLEGCqJZ2Fy6BB/5iKDDKCvId3glWiJJ7KpBlOMUNx5XFcTGYgrsrUGk291DEGepYERRZhGWunbNMXEBbmMxLSNXEB2B39bs3wQrjLZn26qkjDAjqylOxaJYZ4I4aTGNCAq1IpYMBzP0eJcgJchB1CiI+8D3NamQEryFTrUGMZZWzIma5I9LCqJu+QSNgliGGY9cF+j16JQJoozY2JjZoZ0KbpmiIYR438RD/zH7EpCPXe8G/r/jPrDRqtsFAAAgAElEQVSzjv0IohxGlE/L6t0Lp3tgM0C3CzduZHWIywFttKc0wcW1nCDmg/1gwIhOYQ+uQQ1iWX268tAaH+Wn+Wm+C4A0gTBUeJ4o1OEZIo4hyibEnCA6DghRa4Jo6mVMTS1k57XVwicuakqThHBHwqXZOBpMDak7HhGxVCH4jcW0gF86PV7Hh1EpaGjHnlj20UiPkwluRb2oI0Esj6Odjr4nIzxwXdLEwc2u9dFWBMz+A9JjaVS1JzYEMYexJoahaTdTrQW2QUGMY4gyki8nSH5DEAuY8chxIMnKn/I2dBauTY8brxaz+E/2edyQQ0MU7z22I6oJygTx5ZeLx6Nh1Qa0dt8ZZxlMBNWstWmjPxyjINpWgzhNQUx3h4xZo5Mduwh8FhdndICnhIr61G5zYWnMA1tP4RET46FkjFI+o9Hs2xBULTVmUaOHv1kf2yzhednkl+gv180WrI6D5ypUUjD78Oo2vHbt1I8xTfWxxTE4clz0QATwGoJYhuvm+x44nSCboPU5lGFMHBeEZJYIMzVaK4hpXv9UR4JYHkeF0CritWvgt1zioYdLFvi1McYWghiPCgVRE3y9M9GQC42cIPa6+Nn5MwqiLQRRDo1N2NfBiF6jIE7CbKC6LshWi7YYs6uK/l4bL9txT54UbjlVKKXuOa0DqRumKohxzCiqun5X7zvjPkWKfnrDIfTWOzlBNL0Qt7awQoUymEYQwx1ZSZ9dXBI4ZztfaG/AyYULBFsRAZIYj3QsAZ/BYPYkrLDUJESmBtFreuiBngTDUCt0nU5BIDxPVAniK1vA6RNEE1CjIglp6fwhwHEai+kEPE9/pW3dCzFv0p3Vkc7880pTRmN9XSWmBtHR92JQw67Kk3XAy8sZQWxrgtjKCIYtFuE4BjmQVYup0yiIZZjrWCz0rKxB1Ocwzs+hh2wsplNgHBmOAwhBuwVpWCzsNp/f4SwTxDO+hLUXhmS02xRJSLu7DCmtVoOA1fMWbPeeMMyANBxC5/wCHgkeSUYQx8SxPWEnsA9B3I0rBHF57ew3ZtsTcHLhAgFRUXMRaUubDTUXUaSDFdxKzUVDEGGvzTQniL6o9GIbXd1hFjAEMc0CvCKjIHo6lr1REKvwfUMQdS/EvLlzbEmri9Eor9dOcXAE+e5fHQnG5EabqUP0Ox5xaQ9/aBFBjIYxLmlWD6yasXQCRgkXvd6ePog2rGX0OZRVi2mTYroHhiCae7TTFZVeiBsvWnAyTxANQZwRpimI6dYOIcUMKVrBmQ86AX0TBoGeIINzenbUaVFu3i7BJq+3IYiOU3xvBluDlfNnO30WyBXSJMl65OUEUTMNJe0iiNFuhEtS1CA2FlOgIIiGiOUEMagSxPDGbE5kriAONbvJCWI2a89cEbMMhiAm7W6mINoRNGSgdnZ1KxyUbrReWoXU2WJqxlFDEL2O3io1GO7Y0RNZSojDOK9BFM1m2x7k1/EkQYwlg4GaaU9ZyEouhjFiyoZpQxALVBREoN1zKwRx8+UZ9n46BTQEcQYwke3GCmQI4ujmqKJCrfTkmbcpGpiJxVnTNZe6F6KTyxo21SEapaUc7EE4qjQEXb549rfC8/hnst3vCxdoMS76Pkl9LduwY2qS9wRJtmOqmgkxg5kEzfVs/u0FTmFPBEbXZ0sQk8GEgpgR/IYgVpEriC2tIBYWUzsUxHhjJ6sjVcS4OE4xctbRYgrTeyH63aCiII4GdhDEOAY5iquBXw1BrCAniEGA6+o6W4UApUhlWl07zABxDNEoBt2FNB9PzZq0gcakgthe9KsK4nV7gr9OAjWhH3ahrB4qVRDE4c2wQhBXl+2YEE4DOUFcXwUMQdQWU1BWKoh5a4A41vVRJSxfrgfrqJCLCYupbQqiHEb5YtnUILbb1GYTZj/sazFtuVUF8eZsmH6uII70wGnIhZm1G4tpFbmC2OrgIUsWUzsUxGR7kLcqSXBx3OIGrKOCCNNbXbjdQM+BGeQ4qbSjmRXiGKIwaQjiLZBfx0JAu02QtX8CrAiqiWOQYZIfkzmHzVhaRV5Lmg2h7cWgsmm6uWFH65mTQs2XRrNBmSAOBjqlDyAdDIs2CcDauiWpLKcAM7GITgf8gDZjPTkqBXFsjYIYx0XaY944fDis7Crh+yyv1OPWMhPKYEBOEAOT2hYnxc9miDjOkjBHMrdsmR3TZkFzAIK4GTIL5ApiXoNYXZQ2i5oqcgUx6GQKorGYWqIgbg2QmTUxwTP5JkA9axChmmRqFETR6WRhTOQ/tOL8ZT1ltUXYQWSbNULUVwGeROVz6OheiOX70IY5UYZprlALFHj17gk8DSYV2nX1GqK93Ck23IDtHVFkMJxB1GMVaxniWF90kz0Q1W511Fi9cPbr2AwqA1PWbyYnXRb1m5kWUMNwWLEC1aEHooGx9+3sMEVBtMNimhOMcUSEvqdMUX4zIe4liKZNgt/2UBQ9jUZbs6m3MMeVDPU3EUElhbaxmFbh+9mCJlMQVUm5sIFgJDvDjCAq3SahpCDWlWCULab9fuZq6HQQqGJBalHASTxOc3XTbNZ0u/Ykjc8aRkFUClSrjV9REGdL9NNUf8WRbnEBxYZpM5buhcnISFNwFnq0KOZBNZZFyOQZREMQZ4BLl+A974F7760SxHRQ3aFfvaMeNkWYIIj9Pi1jMYW81YUN2I8gjstRx75fO4K4u0teg5j3fYrT4mczhCEYKpQTE2JDEGEvQRQiq4/u6FWr2fkOt2ZTODOpIMqJkJpGQawit5j6bSsVxGR7gCRAoFCA4+rjc5z61j+VFUTHQffQbWtikW8+WmBNzAw9RFMIYnMfFnCc0kZNp5cpiMVGzSwVRCmz8xglzYbpbcD39VeSAAsLdCgNolHExsbMDu3E0RDEGUHoFl6V3Qc5qO7Qr72mPts5rZYeTIWAtLeQKYiuVi+isd0K4mBARGnrO/BrkT4LEwTx/PnMYpopiIkmiLNe1OQK1FgiswlR26IaiynsJYhgCKL+rHKCuDubAqiCIBYKYhNSsz88L1uYBu28BlGBNQpivDMiws+VMUMQ61p/CAUxNjWGy8vk1sR8o9QCgm+SVpMozTfbjMW07mFfk2i1NLGIW71MQbRDCY7jbCNCRnk9t4CG5O+DINDnMU2Bfp92SUEkGlsjXpwEGoI4Y5QVxGhYDTpZu68mLANNDDudzPPd79JmjCKLhp4Di+mI0uxYIwWx3dbnLIogWjpX7YOY6DqVWddb5AQjjKs7po2CCNyCIHb1D2whiHIYkeLo5Mtsx9uMGw0K+H5mUWwF+OhzphBWEAwwCqKfKyompKYhiAVBXFoiI4ixVQqiOb40ipGVeu5mLJ1Eq5Up+UEHn6iiIM6aIEoJTjTOa4EBcL2GIE5BRUHs9wkqBFGycf3sFiE2BHHGyAliFDGSJX+NEKzdsziTY5oVzODkLXZpo+1kSdYL0ZZdmjJBzInPcMiI0uxYoxpEIUoqIn2CQBQWUyUgSWZOEHMFMZqwmDaWGkATfFOEbxaAvg9eV6/YDUGcVcy+IYjxSO4h+L1eU/c0ibx3V7uFVyaIScxod/aLmXhnhCwpiMJvCOIkQSxbTPNeiBaE1JjjS2SKzFwz+l50mrF0AkGgv+JWd4+COMs5MSeIMizcGNCQ/H1Q7tON5xF45eRSxebzO/s9de7REMQZIyeIu7tVkhG0WF2v1+kxg5O3NEkQ7bGYGrIRBIWCmOwO84UrgAg8FhZmcHAzQk4QB4LWucXCYooD0ex3vaNIkx+VWUwFjcV0EoZUGDLmeeD3qwQxHM0m0rtQEOXEorSxRE2DOZfCdfB9fe5ykn9z9hJisjtCEjQKYgnmnBkCFgRAZ6JNiQUhNXFs6hDTCbt+Qy4mUSiI3YkaxNkS/TjWc6K2mPoFQfTcxq4/BUZBNN0G/E61UHrjuYYgNjghmBpEtb3DLqXVTitgdXU2xzQrmAnGXdIhNWAspvYpiGWCON6Rlf6VS0v1UjXKdYj++ZVCQUSgotkW5IMmGFKCK8dEmaXG1Fw0ixqNaa0uvLaHXv5lBDHxOO2VjVmQuq7u22VqfQ3BbxY0e+F5RY27m6lzJkFxtDF7ghjvTtQgeg1BNAqi2QzxfXIFMQ8ZSmKGM1aApYREJohYW0wFKu9J2oylVZgaRB0WpVNMTS3wrENqokjhRGHRMgiaDdN9ULGYAl6nGrW8+RULGj2fEBqCOGMYBXF8s3qR9bqqdhNm3gtxcYGACIHKFMSI0ahaIzUrGIIoRMn6NhybGAgAllbcKc88uzBq6e4uOOfX6ZZSvlQUzXzXO4oyghiNiCd2TJsJUWMqQfQ1y8gJIm24ceNUj6usaEajNCP4hUW4URCnwySZuu1q0NBoYzatSsoYbUvIqIWDQmTtSuo235UxaTH1fcBx8N3i3AGMrs92MI1jkLtjHNJMfVJZwatoxtIJtFr6o0lbhugXtcAzr0EcShziPKTG7Cg14+leBIG+P1W2xPN7PmUvzcbLsx9TTwoNQZwh4rhIeBxvjioq1OrSbOp9ZolOR49T7lIfELQZ5wQRmLnNNE31glWIgigCOMMBZcFwab0+/SuBvP/VcAjJ2nl6FJsdaRRb0eYiijRBlM2O6VRMJYge4LgFuaAzM4KolD440wPRKMCNgjgdJmLfaU22Kpn9Yibc0SwoReiADLchiFMJIuAHorAmAsMZE0QpQe6EOCSldjP64JuxtAqj5LudVqnsQtgRUrNTkPzyPdgQxL3w/ew8Zvv+nQW/6GkJbF6fTXjbaaAhiDNEZeE82K2QjLX10z6a2cNx9CKhtbZAjJu3ukBGgJq5zbQcUFMZ4MOqbWvpQvv0DsoCOE4xsewuX6FP4clXFtQg5hbTaJRZarL9v8YWlWNfgug6VYJ48+apHlfew1IB4/FEqEJjMd0PuYLYmlAQt+WtnnYqGO7ojdDUpNE2BHFPDWKZIKZlBXEjZJYYjzW58Egyi2lx/po04SoM6Xe7QZHsjQOxBSE1OyEu6Z6WQc18uBfmXjQEsb0YVAjixsZsavNPAw1BnCHKPRDV7v/P3psGyZakVWLH7xprRmS+ly/fXvvS1TTqphsBDQiaZQBhmGBGQ7dkWjCbGWwkJBMj/Zqx+SOz4Yf+aMwkMyTDhA0MjJrGoG1gZDAzYOzD0ltVF9T+qqsq35prREZmLDfu4vrh7tfdb9yIuJEZS+VLP2ZZLyIjIjMqPe69fr5zvvPpu+hLWxdLhRLwPKDc8BC7LKgmhsV2h+Hq+xBzR1xQCtrXL9qNqxfvSpn2Ia5d1xVEHq2frEgQF8mcUQRYwwEiNdbbWExTjFcQPxwWU6EghpnUPVPxzodts/Wz+CzLtAdxRaNKVARdqSBavJcUuNgEMbcHEYBTsjWLaW/FFuEgAMKTAWxE0o3BQ4bMsahDrKlVKaUEkVlMVzuPNIqA4clQsQkzuz5gZsrmIU2F5myptF7SFcSjx5dGLeX/jBBSIoR8iRDydULIa4SQ/5V//ylCyF8RQt4hhHyBEOLx7/v8/h3++JPKz/rH/PtvEUJ+aBnvf1FQZyDGJ/oZY+PGxSMZAFcQfSCursHHQA4J/hDMQswliP0+I7ECto3mZWfktY87UoJYu4oSAnlBHLLN4KpUxFSBCiMgiRWLKeu5MASRIUsQhQIFW25QA/ig+6shiEkCriAqCrDpQRwLQRCdcmZUycnqWxf6PfYeEljGYsox1mLq6wSx314tQRwMgLA7hJMSRJqunzmX6kgJYtlXgtu4gniyOtWJkfwANmJ5PjVrOBapVdhmhUqvWdEyJ3p9veXoccKyqG8A4Psopf8RgI8D+GFCyLcD+N8A/HNK6XMAWgD+Hn/+3wPQopQ+C+Cf8+eBEPISgM8B+CiAHwbw84SQc5sIohLE8ERXoTZuX8xSjuex4etxpY4SAmYxBYBh+OEkiN2uHGQMAK7HhhxfMKQEsbwJD0NpqQlXSxAFwRAl26FWMTXBCgJ5CqJlAbZN0mopBcFwd7kHoUYQeQ+iqiCainc+UgWxxBY2JYi9DwFB5LXQ2FhMU6gEkVKFIJYzCmJntQpwEAiCGI/0rxmLqQ7LYl+O78C29HEzvaPVWb0HAxZS4/CQGqJYTE3BbRSE6KMuSL2OGpT+sOFw5e62RWEpBJEyiL+oy78ogO8D8Bv8+78M4Mf57f+M3wd//PsJIYR//9copQGl9D0AdwD8x0v4X1gIUoJIKYZd/YRx6ZkLMmk9A99nF5qoUucWU0EQVz8LMW8GInq9dB4Ue9C90ASxW7oEF8O0KT8Zsn6jVfVciDVLeqwAE5mKaS7y5iACgONmUhR3lzvzKUsQ0w0NYCymEyAIos0j2VOL6eqnXKTzNFmKqSQYnjfpVY83CJHHXByrBNHVQ2qOVzfmIo7Z8Rh1hzykxtXWz5xLR+E4PDCKz84Tx2HvaDWR7Oka9oaw1TV0TEjNJLguOz/FMYBaTWujQTBEq7Wyt7ZQLM08SwixCSGvANgF8HsA3gXQppSKktg9ADf47RsA7gIAf/wIwCX1+zmvyf6+nyaEfIUQ8pW9vb15/+/MBWkPYhCwGWMCloWNWxfzSPU8bjEt13hIDTeUfQhmIeYqiL0eAiilb/diEkTHYcpv0lgHgNRSQyO2mV81QRR9osO06m2a8lXkKYgA4LiWRhAHe6shiFFvCCSxVBB5LLtREPMhexB1BXEQkDSufVUYDNh7ieBoxZqLrkCpfYiSIDp6SM0K5yCmI57CAAQYsZgacjGKlCBmlPzu0WrWcSCMasMAFIqjxpD8idBmIZZKqBDFU5rEaD9YcRLfgrA0gkgpjSmlHwdwE0z1+0je0/i/eWPG6YTv5/2+X6CUfopS+qnNzc3TvOWFI1UQj4/Rg3Jkej4uXb5Ak9YViNlBTrWEEgag4FW3D4GMrxLElPB0uyzdUcB10byY4i+bh9hsIoYtLaa8qWZVBDFVoDhBNFXvfNg2+xKhPilB9DIE8WC5CynWLzgSFmGPjQOymUXYEMR8iPWkfhkO4nQNaRittF8miSmGQQKApiNLRMjJ2trq3teHAarNNA2pqXq6xXSF+9CUXPAPkLGYTodUEDNKfjdeSaFGrGEyGCKGAwqSppj6vvwMGuhQLaYgBOXMvqH93uMpIS49fodS2gbwRwC+HUCTECI+kjcBPOC37wG4BQD88QaAQ/X7Oa85d1AJ4jHq8gHfw6VLK3lLK4dQMty1Enywsxmbhbh6i+k4BVEniBezBxHgNtNKBbFdknOfKAHieOUhNXGXFRuijKXGEEQJVUVMCaKfIYiHy11IQRCHR7KHlBFE0zMzCUJBpH4JDkJpU+SpwqtCfNxLizRD+LAIAMLeW70++bWPO1SCKG67VU+zmK5y7dLCwoDdiEyxbSrSNOhSCQ5i2c8dRpJwLxHid9IgxJC3xojroVm/8fA8RUEEUK7q0Set7eU6a5aFZaWYbhJCmvx2GcAPAHgDwB8C+M/50/5bAL/Fb/82vw/++B9QSin//ud4yulTAJ4D8KVl/D/MG5RKghi2u1ofm1O6uPO9RFCBu8bGXACCIK42pIZSPeFREETa7aILZZd6QXsQAU4QCUFUX1dS2wgQDlduMU16ASI4oEBaMfU8OdvIYBxBzKQoLnkOm1QQhWqhKoiGII6DIIiJ58NFJG2K0Yoj9tsnaXJiAC+NjgdMxH6eguhWfVAlM7E3WF2kfkoQ+Q1jT5wOoSAmfoUXavhxGEYrKZqKY58GrJ8bAPt0mX7uidB6EAH4db1hun1/hYMtF4hlCcrXAPwyTxy1APw6pfT/I4S8DuDXCCH/DMDLAH6RP/8XAfwKIeQOmHL4OQCglL5GCPl1AK8DiAD8DKV0dab8MyAI5OY1OurKZD4AG40Y5GI6TNP0RK9ZRgR2IWIEcbVjLoZDHnHssdviRGH1ukgg5V6vbF/YND6xwQvr6/DaYu6TBQxDdLur8R+lBLEfpEUYAmoqpjlQCWK6QS056KkKYnt5BFHYXQkBBh22kEN4qKAL2OzNXnRSMQ6pgugxBTEtQIYh+t0EqxqBHHe6uoJoy8/WRVcQxTEXRSJBGKC1EoABKAgIKMKQagrjMiHVp4C7MZyUXADGYpoHMS4o8ZmrJp2fF4Xo9bB0p1hqMQ2G6TlBFEwNQRwPQRDFPGd/Td/ktR49nnMulnKaoZS+CuATOd//BnJSSCmlAwB/d8zP+jkAPzfv97hsqCMu0D0BgZQyLl26oOyQw/eB0qUahhADlS1gGOD4GCu7OObaSwFY/RNAIYjNdXJhyb3vsxOpVavCAmPQCR8MvOoxF1FviEi7IBqCmEWuglhydAWxE7JKyRI+5OrQ8G47TEMVbMQmdW8K0p5SYsO1EgwTZQ33u8Bzq2Fj0VE37V0L4KGkEETTg8j+VT/3cbkMF8f8GKSpRXgVZFpcA5NB5lxq2SiVoKnBBgxSQSzDhbR1IoxW4qoZDNjpOx4Yi+ks0EJqAHgNvRrS3lvd2JJFwhzSK4JGEI91//LGlYvdKex5QOlSFRQ2fARMQQyZhJcmvy4ZuQE1AKzuifa8xsbFXrtaDXAalXQUAYUFhEONVC8TaQ/iINAviKZiOoJcguhZoMqo2UHiYlkHoUYQOzFi2EhgpRZTx7nYoxEmIVUQKf9X7WPbWdFJFKIH0YPFQ2pUBfGiq8GqxRTgimK5rFsTo9UV22TASairT4ZcjEVKED1GEKWCuBqL6WDAi+xhPz0OCWBmyk6BFlIDwG3qH/hWa8XR0AuCIYgrgrrHSrp6U8il6xfUo8jh+0Cp5iDyq/osxHB1NtPcGYjASDxn48rF3rHWaoDbqKaaUwICDFezqaFUVRCjjKXGNpaoDNRZiClBdABqy6LHACXg4GAp70esnWUBg5OIp15Kgl+rLUXIPJdIexATwHWJPiphb0XVGjCL6TBVEI3FVEUuQfQ8uIgkQUwS9NursbOJIikNhmmxTVhMDUHMhwipYb3Aw5US/ShiX0kC2GEgRz4BgO2YNZyAbEiN09TZdPvo8aRSj+f/1TmAKhqGXX1o6qWbF3vn6nlspl5cWeOzEDlBXGFQTa7FlNIRct+4erHXrlYD3HV58ky4grgKO42qQIX9KLVFmR7EfOQqiBmC2Ed56QQxjtmbYhXvmG2xHBOqMAmagugS3Sa8v7pAhajDUkwTrl1YvH/N84warPYgpvcJgWsnUnkC0Hu0fAU4DBmxcBwgHMQjFlNTbMuHriBGch1XEFIjFGDLAhAE/HzKCaI5n05ENqTGbujVrFbXW/l82UXAEMQVQSWIQU/P2dl44mKXUn1fEMQ6VxD5x3Q4RGtF42ZyCeJwiDhRzgqWhcbli73LqVYBZ73GBiiDDwYOw5UQRDV1NugnGYupuSBmkUcQXTdHQdzZWcr7EQQxDAEEgew/BMz6TYE619L1MwTxYHXD9MSYi5jHHxCuIF509RDI70EEANeBtn693eUrwIJclEpAMKAjbgxzLOZDKoglriDKkJplt12INSQEvODmSoJo1nAiVIsppUBSW4MPqeRHwepGeS0ShiCuCClBjGMEuoCIjdsX2wyeKojlmm4xHQ5xdLSa9zRuBmKk5jy5LhrNi+15cxzAXa8rITXWyiymgiBaloj1dpWeC8dUvTMYqyA6cgTPACXg0aOlvB+NIA6HGIoRF0BqMTXIh6Ygerbeg3i4ujkX0THrfQr5Od1yzAxEgVyLKf+XrtgiLK5/ngcMA6onQhuL6ViIPunE8eBjqIybiVZLEHnBzTIW00IgRKb0JgkQeDWsQ1ErwiFae9Fq3twCYQjiipD2IHa7CFCSD7gempcu9nA2oSBGnCAmCkH8UFlMu10MoSiG7sWdgSjguoCzsZZeeOiHwGJKKYAgQKT2XBiL6QiKWEwHKAEPHy7l/WQVRDYD0VS8i8C2ObGgbJal1oO4oh42ABges4IfOxYBwi2mhiCOJ4hZi3Bvf/nVNkEuXBeIg4gn0VLmFDH93GOhKog+AlmoCaOlB+4N1AlFqWWfO6AcE1IzDSpBDBMHa75+Hm1/43AF72qxMARxRUgVxG4XPShn1ws8aF3A8xgRS0qV1GJKgQ+NxTQlO70e2zALuB6azaW/tQ8VbBvwN9dgIwaFDKlZpcU0jqFYFGXF1BAMHZYlVSeB3B7EJSuIwyFSBVEo02ZDMx2C8NslW1egjoZjXrF49I/YooZwQJCAOIYgCuT2IIJZhJMVK8Di+kcIgDBM3RgAjII4AWkPouPBQyALNXGE46Nk8ovnjFRBTGIgHGZCaswaToPv632I9ZredNj6xoo2pwuEIYgrQJLoKlQXyk7VkAz4Ph8UXCuhDHZWi2GvzGKqNujbtr52GkE05B4A4F1pwgXbCFIQ0OFqFERBEKMIac+F2sNmqt6jEKRCxHnnKohLIohi/VSCqK6fIYiT4fMwbMd3hNYDgM+yXBH6HbaokbC3GQUxhaogUqoSREtXEA8HOa9eLEbVJ51cmGJbPlIFERY8m2rHYWdvuUq+WEPaZwWGNKTGtgEQs4ZTkA2qqTV0+tTeXt34oEXBEMQVoNeTGzBv0EGo2BSdkqnkiP4Zf60ElzcCs162YCUKoqoeUqoriD0oi2UspgAAb72KEhkCfFOTUBbEFMeTXzdvZC2KbFMqFaiLfpzlIZcgWqu1mAYBckJqjAI8DZ7HC21lD2qtu3+y5ANRweCYyWMhHEMQMyCE/TkoZZtQSRB1BbjXXr4CLK6BFo2BKMwEnFim2DYGhLB1tG3AKzFyKNby+GA1BBFdZlEOM0Uacz6djOwsxGpDDyRs319dOvSiYAjiCqB6z+2eXnVo1BIz2wtsc1NeL6dKlFAQV9GDqBJEldyXh0c4xpp8ousZggjWM+OWHThgm0Ex6mLZQTWjCpRJwZwGsSkVNlOmIMqe6FWE1AQBlJ4Zo1oUhSi0OWW2kRE2xX53dQSxx8lp2v9kLKYaVBUxJYgfAotwOgMxVZ+cdE8ylswAACAASURBVMQFQEyxbQKEzdQpsc+6OA6PD5cXaiJmINo2EHcZUxwKmzB3iJg1nAxBEEWhu3qppD3e3lm+sr9oGIK4AqgjLqyuHmXVbD6Gw1ROAd8HSpercDnJWKXFdG+P/VutQkseqwwOcQxlZ+O6WFvDhYfjAG7Fg6PYTFcx6iIliAE1FtOCyFcQJUFMexCXMPRJEMTBAFxBVCympgdxKsSoC6vEFlWQjH53ddeYQZd9sNL+J158MGvJoPYhpiE1ZUdPoT1eblpikjCCSAgU9clLE0wBQy4mQdhM3TIjYuI47LSXV6jRxpQcsTtsDc0xWBSepyuI5Uv6h761a1JMDeYAlSBms46bGxc7wVTA8wB/vQKHt3UnsIEowvFRnDbxLwODARv7Rghw44a+XHa3o1V2a2skrQBfZLgu4FZcXUFcwaiLNOSkMwBAEQmCaFkAscymJgeCIEaRtEdRYoESrj6hDPT7WHQEn+jDsm3264wCPDuEgmiXMwRxhYXuQY/trvT+J5jCGoc6CzGdRVpxtRTa3slyw01Eoc3zgLAjFETdnmjOpeMhlCenzBh/qiB26NKGq2sEscPk4HTMhWPWsAiyPYjlTZ1Rt1uPn7hjCOIKoBJEmiGIjc2LPWhdwPeBcsVCXK3DR4BYfFSXrCJub7ON6tYWO7mqKhjpHmvPbawbcg/wimnVT9VfpiAOlzr3iVK5sRm0VUtNklpqDMEYhSCIYcg3NQ4AEFCPJZ6koUwLtpkKcu84/Jjr9zMKopmDOA0pQayyNUstpn2yFAU4D4O+VBBND+Ioci2mFVfvQeyuJv1SV58cQxALQiiIVrnE073ZWsaDCP0lBdKK3yMIIoUaFGUspkWQtZj6V/R+otbR40enHr//o3MAtfgeH+uySvNqCQZso1oqAXGlzkddcPIVLK8PcTiU++Dbt9m/Gsk5zpJ7FwbcTlPzYa9QQRQKlOMA/SNWMU0tpnxTYyymo8jOQhQbVuqx89KyCSIhQNiPgHDIZ+dRAMQQxAIQBNHyHIDIJMw+9YHD1czsGvQZMU3HJBh7m4Z8gujpBLG33JACtQdfEkTXWEwLIh11Ua3Dx1COLBkGuptsgdBI/jE7l1IgVRDLZWasMRiPbEhNliC2u4+ffcx8JFYA9aQQnehJVo3rRtYAeA9iCYgra/BVgjgMlkYQ795lJ4PNTXkBVAlicqw31TW3DLkHuPJUL8Hhas8qehAFwfA8oHsoo/VtxKmlxiiIoxhLEF32wAAlloi54CRTsX5xjLT8nYCwbbLrwsSyT4foQbRtdkeQjB4qSwsaUhHHwHBAQUAxhK/1PxkFkSG3B7Hq6z2Ig+USREEufF/aE6VFmL0vQxDHQyeIgST7gxURxBNm1weQqsBm/aYjqyC6Wxva40cDH0n8eNlMDUFcAdKTAqUITvREsuYtc6UEFAWxXEMZA6XqthwFMQyBBw/YbaEeAgpBjCLEQ3WeGEFjy1/8GzsHcBzAWyvrCuKSU0yFvdR1ge5RBArIHjZT9R6LLEG0LPZFPdnHNoS3NAUxDAHxwUmLRHznbAjiZKQW0xyCGN9fDUEMgwQECYbwNXub6UFkUHsQUwWx7ms9iP3AWqpFWCiIpRIw5PuVrMXUuDHGIyWItTV4COReJhishCAOTuJ0tBrhKabmXDodnqf3IEZeBXVLbmpoQtG593jNQjQEcQVITwqDAQKq2BJtG01DMgBIBTEq1VZiMb1/n50INjb06nZKEHs9hFDWznXRaJrDCeDKU6Oc9otRbjFdpoIoCCIhwLAbpp8fcUFU1TEDCbEpDcN078eCajzZG72MURdi/QRBZD0zIrXD9JAWgSCIhAC+m6QbUwqCo/eWbzGNQorhkHKCqCtQxmLKkGsxzaSY9mgJy6y2aRbTY0EQzQy9ohDXmqRa4wqiIIjBorO+UmgKYjcaURDN+k2HZbFjAGDOsv6AoFnRBZ7WO/sreGeLg9nRLhmUAh/9KPDCC8CN2lFm0LqHZnN17+3DhFRBLFWWbjGNY+DePXb7iSf0x1KS0+shgELmXdfMQORwXcBdq6YW04SH1KzCYhrHAIIg7T8kAOCYC+I4WBYfbUFlT4rjANTJEMQlWUyHQwC9ngxUAADXhe8bgj8NgiAmCVAuUa2Prf3+8gfKBu0+QjigvJPUIgCIhVLJrKVALkF0Cagt/0A9VJbaQ5rtX6MwPYizQCiIcVn0IPLjMAhwfLT4wKEwZNdBQVSDXpwWt0UPorkeFoPY48UxyxJp1vRI/fZ7rRW8q8XBnJaXDEKAH/1RfudP3sZ/BYUReoZkCFgWrypXKvB4YzcFQJagIN6/zy7QjQZG1iNVELtdnSCatUvhOIDTrKUKIrOYDlZiMQ1D5MxAdMyGZgI8D9ooGceRPYjAchRE8VmJInZHSzB1XaM4FYDoP6QUKJUIQoUgtu4tMVKYo7fPXBeCqBKb/Wv6DyXyehBtm88i5R//EC6ivRacW7eW8p5UBXHYixCD2ZWF+mTcGJORKoiOB89OkMS88kaFJXGxqoBK8MMQoMGQ95DyI9EoiIWxvs7+TRKg3QYTdJRLYesDYzE1mBd2d9HWCKJnSIYC3wdKjRI8sN1+DHvhPYiUjlcPk0RuXEmvy2bCCRgFMYXjAO5GDZYaUjMMlzrmQiOIqYLIq7W2bXpmJsDLTNphBFHaqfsoL5QgUop0lA0hGB1xYQhiIagKYqlqaTbF9sPlD0NkBFEmclqGII4grweRkFEC1t9ZzkY0itiXZbH3E3RDXX0yASdTkfYgJoBfsTUl//ju4pV8TQEOkM6UJeJ66JiCaVFs8FwaoSA2NnQK1b6/RJvUEmAI4goRPdhFF7J0QzzXNOsr8H2gtF6GC+Y3YwRxsRbTdpuRi0pFngwEul2ZDVCJjtCBwghdQ+4FHAdw62VhCmYKYhKjdxROfN08kacgWukMPVMxnQRBEAnfx/g+kDhSLT/ExkItpicnbFNaLssexKGqALuuWb8CEASRUqBUc7Sgk9bOcMIrF4PeQR9DuClRNQRxFHkWU/X7Av1HyxkGrAbUAEDQlfZEYTE1x+JkaASx6siQGgDHDxafUjNCEINAptACZg1nQLnMvuKYF97WdWbdWkHhbZEwBHGFOLqrVwHXatTMolHgeUBpo5ISRGZVDNE+XJxvf5/3GG9ujj6mKmDVoKWrv65r+kc5bBvwfALbY7ZgUTHt7i9pKjAyPWxKD6J4g6ZiOh6CIIpiSLUKUGWH+ghX2YESLobwt3gbR7PJe357PX1DYwhiIWg9iHV92Hr7IF76++m12HEYG4I4FmMJoquPtujtLUepUEdcAEDQT0YUROPGmIzUYpoAfl2fadnZWXzfhZZgOgAQBBiqPd0mxbQwXJe1PolZiO6GfvJq7y2vCL4MGDqyQhw9yMzRMwRDg+8DpaoNx2cXJBFU094NJr3sTBAE8dKl0cdUglgbHuBIVRBND6KGUglwS2y3Iyqm3cPFrVsWQkEcDpHbg2g2NeOhbkwBdkGk1Eof2MEWY497ewv5/cIhoBJEYzGdHaIHMUmAUtPXNqaH7eVe+uOYDVmP4CDm0QeEJ5gagigxniDq67UsgphVEIf92FhMZ4SqIHprvq4g7i/+mqgSxONj5FwPjYJYFIIgilEX1rpu+Wu3Fh86tEwYgrhCtB/qikrjkj3mmRcTnsdIol0tgQBIOEE8aYVaiMa8cHzMLoi+n79pUVM4/ZNDFtbBYXlG1VBRLgO2b/PEQnaa6bWXZ2sTBDEIAPT7+gXRNRXTSRCbVOFmYAQRqbS4gy32wAJspmr/oU4QjcV0Vtg2H1FCgVJDH7be7nty978E9HoA7bPr3ZDPYLPMiIsRWJYMFiKKaGi5zI0h0DtYjhtDDaiJY6DfpzzB1BDEolCPQ69R0Y7DzuHilXyVIHY6SC2mjmm5mBlZBZE0dVWg1SY5rzq/MARxhWjv6hvm5qY35pkXE77P/d6VOnwEqTUJwTDdRM4TBwfs30uX9IuzgKogko7+BhrrJPc1FxXlMuCUWKVZ9D51Owtg9TkQ/QGWxS+OfGZlaqlxXLOpmQAx/1B8nqvVMQRxAUE1x8ds/SoV9ut6PaQhNarF1JCK6dAsphULiSMlqRbWgd3dpb2XXg8IjwdwEGEInyUoOuyDZvrudYgCjRhNAACOZ2kKcP9wOQQxqz7RYaSPDDIEcSoIkQqs06hovcAnncUrTiMEkYfU2ODXY7OGheF5uoKYtZrtHJUle3wMYAjiCpHtpWtslcY882JCKIhxpYbSEmYhCnvp5cv5j6sEkR7rzeXNSybnW0W5LAY8SwWxe7ycE6dQDz2PK1A5CqKxmI5HliBmFcRHuMoeWABBVO2l/T6/1vZ6PFafayhGQSwEfcwFQG1JENtoLnxUiYpeD4i6AVyECOCxBEU+289YTHXkzkL09fTLfns56q+qIApyERlyMTPE9cZdr+kKYnexrrHsDMTOEVUURLGGxlFTFFmLKaq1tNAFAPvJOvrvPljNm1sADEFcIY4yfuXmTVMWV+H7bGMTlxhBTL37w2EaZDEvDAaMANr2+F5QlSAmHX1mQ+OKUX9VlMuAVWHJBmLdhv1kIdbgLEYIIlcQjUWxGLIEMd0Aemw922gigLcQi+lIQE0cA8PMZthxjIJYAGqKqe8DNKsgLjCJNoteDwhPhnARSjWYf9DMWupQCaK4LYptAr395QyVVUNqjo6Q9q85JvBrJgiCaDfXNKvwYACEg8XZTFX1EAA6hxFAE2nZJwSwLHM9LAjXZY4aIRKenADX1/R+4HtfMgTR4KwYDNAeKIPWQdC8bs60KjxPEMQqfPSlgrgAi6kaTjMuSTYliEmC6ESPM25sGUlKRaUCkFIJLiKuIgIIh+kcyUWCtzrBdYHuCU0VRNWiaDY14yEIojgOLIvbAJUBibu4MncFKkm4SgG9/5CBb45dFwAxG5qCEEvmONAq3QF8DO4uJmQoD70eMOwOuYLoawTRKIg6hGqoKYg1b+kEkVI9pCbXnmgZglgEgiBSx4VrqcIAxfGdnYX8TkqBw0N2OyWIe2xB04IpV/HN+bQYsgri0RFw84reKrb98v4K3tliYAjiqrC3N5qCuW6WQwUhfPNQLsPHMA2pwTCYu4I4zV4KKASx30cIxVLqOGhsmIAhFZ4H2FVmY0lHXQxDLehnURC8pVIBksEQSGJpUbQss6mZgqyCCIAl9PqSIO5ga+4EMdt/qBLEtP+Y75jNhqYYxHgCSoFymWqPtd5fziw9ymo0CHsRVxBZD6L4oJkeRB1CNQxDlSCWtPTL3uFAzqFZEIKA/QrXZadN1WLqKBZTY9efDkHQkgRwXaKpiIsgiEdHwMsvA++9x+43m2wtO/uMzKQWU140MufTYhCJtL7P/p5JAmze1N1jd99YTsLwMmAYyaqws5OZo+eZMRc5qNcBVNgsxFixmM6zBzEM2QmVEGBjY/zzUnLDY/dTmBmII3AcwK2W0o2EmGG5aILY77M+NsviF71+JsyB2+wMQRyPsQRRURAf4ercLYrimF5fZ/+K/lFAJhiLHbOxJRaDShBLZf1y37p7kvOK+WMwYBspEgxggWKY9iAai2ke8noQnYoLSmQRsh/amHuVNANxeIvxTarF1DYJmDPBdeWoC9+HPuriG/NT8vt94LXXGDnsdNgp+4UXgBs3WK0t7jOCGMPWijTmelgc2aCa9du6BWL7fRNSY3BW7O7qBNEzJCMPjQaAchkeAiSwWeVtzhbTgwO2gWo25cU5iyiSXMPqd9GHUjZ1zQzELFwXcOqSIFKQpVhMxabmyhVuj+K/kGoWRXNBnAQ7Rwyv16ERxEUoiGr/IaAriKlibwjiTBBLliSAX9EXtv1gOX1s4pi3Bqw6FIgeRMdYTPOQG1LjElBPtqT0UQbu31/Ye4gi+eNv32b/dtoJEEe8B1FYTC1zLi0AERKTJIBf1hNpO+8fzuV3RBEjhnt7rED65JPAt30bcO0aK/aJERdpywcA2A4IgVGBZ0CpxK4/Ik9h7Uk9yfTujpvzqvMJQxBXhd3djMXUKIh5KJcBr1HmvWy88jYconUwvypNEXupqnxV4w466tq5niGIGTgO4NZLaaVZKIgnCxQtKJWc5dq1rAIlLIps92U2NeORRxCzPYgpQZyTzS3bfwhkCWK6UwZgLFFFoSqIXlXfuLR2w6W8h5QgBn1QEBNSMwW5PYguNILYRXWhBPHhQ/b7m01pAZb2RE4QbRsAMeSiAIQ1MUkAr+LoCuL9zlx+R6fDAtoqFUYMn3xSP5frPaR6yJAZ0VUc5TI7Z4X89Fl+ckt7/G57DTRZrP17WTAEcUWgj3Z0guh6phcjB74P+FUHjsPOYCyohqL9aDD5hQWRJFK5KNR/CKAWtUbUX0MQdTgO65sRFyIKAsQxeq3FxbMfHMgLZKOR7WHTLYqGII6HGk4j+F+tBrao/MEdbLG/bWbcy2nR6bBjsVqVm2KxfhSQij1/0BxvxaD2PnlVvVemfbD4Id2AqiD2MOREX9jbKpX8gsRFRl4Pou8DiUIQj1FfGEFMEuDuXXb71i32bxwDJy22Iw7h8IATY08sCk1BrLm6gvhgPlVTUWC7dEkWhkYeDwJdATYW4ZlRLrPrlCCIUX0dVVvuR4PYxt4bj0dQjSGIK0LnXkc7SVSqJL0YGEiIURduhW1uROWtvTMfgthqsYtfvZ5/UhVQCWI1aGXIvSGIWbgu4HoEtsPWS6xb9+F8qqV5EPbSa9fYv6qCGGcsimZTMx6E6DP0AKHykIXNQsz2HwLqiBIlwdFx03OCwXQI0ZdSwF/ztXCMw7a18KATQBJEMuhjCHaSFT2Ixl46ijyLKRtTIgl+G82FEcSdHVZoq1blHHChPjEQdjRaJuCkKFQF0a97uoK425/wyuIQBHGc0CDWMISnKIiOuRbOiEpFVxCPOgS3Gnqh9O6XljdCaJEwBHFFaN/X0zqa62OeeMEhRl3YfKaeUILae/MZqFfEXgroFtNaf88QxCkQFVPbZacYscHv7sxHccoiCFikNyHAFnd8qAQx7WEzITWFIAiimPeU2sg449jHZRYaNWeCqNrsxfoNIJVoeK5GIg0mQ1UuSjVHCzppR1XMfV5QDiRBHLD5mUBqMTUEcRR5BLFUAqgrCeIRGsC9e3P/3ZQC29vstug9BLIEkYMriMZiOh2axbRR0sSB44PhhFcWRyGCGAQYGgXxTBAWU9GD2G4Dt69lRl28Mp++0lXDEMQV4eiRXjVqXhqTjnLB4fvsy6qUQKAoUZ14LkPXhb300qXJz9MUxO7uiD3YEEQd4oLoeGwTIdbt6OF8qqVZiHa4y5elaiIUqFg9zbkubBtGrZ+CrIJICCdvnlTy93F5Lkmmef2HACcWvR76KkF0TJjXLLBtdiyyFFOCxJPSawvrcw8ayiIM2Zdts5EzQkE0BHE88noQfX+UINJ781cQ9/dZTa1UYkFfAipBTAS5MRbTwtAUxEZZVxA7Cc66men12I8Q+6U8HB8jtZiqPYiGIM4GtQeRUlZju/WE7pO/+9ZyAsAWDUMQV4SjPb3i0LzijXnmxUapxL7ichU+BrKXbBicedRFELAIdseZbpNRCaLd2pfvA6xHcpI99SLCshiX8EuQ4UIADh7Op1qqgtJReykgFagQLtuQAoDrmKb8AsgqiFHElVmlD2peSaZHR+z31GpSPYljLv72enKwOgC4RkGcBYIgJglXoZT1a6O5cIIo1MNKBej0nVRBFD1shiCOIq8H0XEAtyyLyDFsdO/OX6UQ6uGtW/o5stNB6qlLr32GIBaG1oNYdUAdWaHs0PqZC22iwDbpeJIhNZ4yx9IxBHFG2Da7Vvk+uy7GMXD5WV0huLv9eIy6MARxRWgf6h+gxlXTVJMHx2EXIFquwuejLgDMZRaicFetrU0nDCpBpAcH2mPNTaP+5qFcBvwSAUBSS83B3vxPnO02I/ql0rgeNqVi6rpmQ1MAWQVREsTMLMQ5EAyh4qtrl45D6fVARc8TYGaOzgh1HctlgCrrtwwFMSWIZYrDQTVVEEUKpiGIo8izmAJApaHbHo7uzzcSutViKpPr6oU2QM5AVJUv2DYImdy7b8CgWUw9IHHlfu8YdZkKdEpMs5dSqofUGAXxbMjaTOvPXNEe3955PPbzhiCuAkkyQm4a103W9ziosxBTu2AwP4JYxB6qEsTkQB9Q3LjyeJwM5g2RQKsqiPv78w/FEMXXq1d1oq8qiIYgzobxCmJm1MUcLKZ5BDHt+e31ZFEIMArijBB26lRBdOVu/ggNJPcXG6YgCKITDXCMGgJ4IAAcJACxDEHMgWXJBGFL2aGVG5kU2lbCKmNzgmhpvHlT/72AGnDiKOqTGZFQFJqC6AOJchx2sHZmgijCpMcRxCDgDuHhEBSWdGQ4trkengKVip5kWn7mhjpdErud0jwPzZXBEMRVoNVCmypHsm2juWmaosah0QBQqcBFOFeLqSCIRRSJlCAGAYYDJR6eEDSumBJqHkolwF8rgSmI7FRzeDjf3UQQyKAhteqtWhQjOMoF0RDEIshTEK9eBeDnzEI8A8KQbW4sKyegJo6BYaDZueE4RkGcAWoPomXpdkAKgs72GU+iCoKcCTaCIA7bjPEP4cNBCOKaGYiTIJRDjSBWLM3ifYQG8ODBXH4fpTIoKqseAipBHJ2hZzAdts37SCmrsamJtMeonylwKI7Z/oSQ8RZToTAiCGAjlo4MoyCeCtlZiN3aFq5ae/IJUYh7ry8usX1ZMARxFdjNhJx4ntn0TIBQEF2E0uJyRotpFLGTqmVN9u0LpIrGyYkc2g0Ano/muimh5qFSAdxGGQRJGmzQ61IRLDoX3LvHqrKbm7rVKbUo9vtw1Auia2K9i6CwgnhGgijUw0ZD3wyrCbRidh4cFyDEnCtnQHYda3X9kt+6Ox+b4nvvAX/xF6NCiDgOgzYrpwei/4n3r5nZv/kQNlNVnSuVoBVojtCY26iLIGBEw/O0Q1z+riMAgwFCtX/NqE8zQYzmcRwgUQKHTlAD3T69gnhywohntTp+pmhKEHs9WSwFANc1BPEUEKMu0iTTjoXbTZ0Q3v3y+R91YQjiKrCzow9adw1BnIRmEylBTNWEYIh26/R2RaEe1uujdposhkNZHbe7HTm0GwB8k2A6DqUSQGo1lBCkCiKCATItnKdGFMkCuhrJDnCCkSRAMIAFRfE1CmIhqMoTwCqlV65gtAfxjBbTPHspIPtHAciCDJdVjMW0OLLrWFvX+6Xbc0gV7vVkuMk3viHPrQl3QBIC9PakgugiTHeyxmKaDyenrd33oVXB5kkQhUMmjyxEkTweI9haorAZcVEcagHTK0kmR0Fw8t5eziuKYVr/ofocdLvaLFS4niGIp0BWQTw6Am5d15No7746P3fGqmAI4iowoiCaOXqTsL4OwPPgWFT2I9HkTJubWfoPtRmIYQsdde1836zdGJTLACoVlBDIaPQwxMG9+UiI9++zqvf6+uhGU1WgpL3UAQgxBLEAbFsvnFDKVFqVIO7iCuju3pki2osQxAF46Z0TRFNMKw41xRQAqhu6PNTaDc/8O955h30+hIXu9dfZxqnf5+M1SkD7LmuSShMUuYJiLKb5yFMQswSxjebcCKK4xuWRBdHfhn4fLkItMMqQi+IQZDpJAL+mVwCO756eTMxKEDV4Zg1Pg1KJ7TmiSNqzbz+tt4ltv7WYkV7LhCGIq8Durq4gGovpRKytsc2qU3KRgKRko3WvO+WV43HagJraYH/EHmwIYj7KZQCWBd+jWvrdwes7Z/7ZcSzbNrLqIaATjLR5nBMMQxCnQ1iVVJJYKgGVqsWsnmCb/TYawN7pqt+9HlOYXHeUKKgEfyAUe6Mgzoysgli9pEs+7f2zzV/b22Mk33WBT36SnU+DgJFEsRetVIDD+2wtA6Eg8kKDURDzIXoQVYLoeViYgjiJIKbkghNE+SaNXX8WCItpHANeVS/UdO4f57yiGAoTxOEQCJUkWkIAx6zhaWBZcnZ2GDKCePNF/SK2fff8tx4thSASQm4RQv6QEPIGIeQ1Qsj/xL//BULIK/zrfULIK/z7TxJC+spj/7fysz5JCPlrQsgdQsj/Qcj5y9Cij4zFdBaUy+y6aJfYVTMdur5zupioJJFV0ZkJYm+XDQgX8HyzYR0DceEpleSYCwA4uNMa84riePSInZjX1vIJg0owUjiGIBZFHkGM4/kmmarqYfYsLgg+BUEvJYis6m4KMsWR7UGsZAhiq2NLn9SMiGPg3XfZ7aeeYh+Ll15i5KbVYnZTgB1vrUfMo5/2IPLPkOlBzEeexTRLENtonincRIUgiHmKriimoteT/YcA4BqL6SxQFUS3rhPEk70+jxmdDUHAvhwHE9ei00FaME3bdDwPADEK4inRbLI/YRSxr42PXtUev7fnp4W584plKYgRgP+FUvoRAN8O4GcIIS9RSj9LKf04pfTjAH4TwBeV17wrHqOU/kPl+/8XgJ8G8Bz/+uEl/T/MDf2HLUSQVwCvbJtZQhPgujzwpMxGJogTXHv/dBub42N2kq5W8y/EWaiujOrxI7YpFvA9tmk2GIG4YHlVW1cQ3z99tRRgaogIw8hTDwFdQYQgp0ZBLAxBEFXilgbV+POZhTjOXgrI9QvgS4uw66FWK3bMGjBkFcRyzdGG67XQPLUCvL3NFOBaTSZf+j4jiYCcwOD7UqlMQ0489h6MxTQfeRZT18VoiukcFMQkUeZV5pwbVQXRUfu5zcigmaARRM9ObdYA0EH9VIm0qno4SSrpdJBWutO9J//9hiCeDiKoRtTXrKeeQAW99PF+N55b3sKqsBSCSCl9SCn9Gr99DOANADfE41wF/EkAn5/0cwgh1wCsUUr/glJKAfxLAD++sDe+ILTv97T7zXViZglNQb3ObBkUJCWIrcPTlWdmsZcCuoJYPXqgE0TPNwRxDMTmwa+6/uL78AAAIABJREFUMqQGwP69sw0I2tlhm89KRdo8slAVxLT/kStQZlMzHWKDqiqI85yFqMbqTyKIfZS0GZbGaTEbsj2I5TK09Wth/VQEv9+XRZrnntM3p+vrwJNPyvthCNCTLhJYSGDBAsv6J8QQxHEYSxAXYDHt9djxWC7np2CqCZhETcA0gV8zQSWIjgOgJNfytKMuithL0+d1RVAUP/55kcYQxNNhJKimdhO3iZJGOxzi7tvnuw9x6T2IhJAnAXwCwF8p3/5uADuU0neU7z1FCHmZEPLHhJDv5t+7AUA9iu5BIZqZ3/PThJCvEEK+snfKCumi0H6kb5Abl0xJfBrqdcCtMUuEUKN6J8mp3FFnIYg4OEAAeWIv1V3TRzMGYvPg1XxJ0gAcniEYg1KZmHj79viqqaogBpmQE7Ne05FnMU1nIc5h1EWnw35epSJ7cwQoles30AiiY+zcM8KypIIoAmPU9WujeSqCf+cO2+hevZp/Hn3iCZZ6W6tx51y3ywNq+LHvsfREUxjNh+uOfs+2oan3bTSZ6pQko0+eAZPspQAnFzySVh0GbnoQZ4P4W6UEUSH7HayNzogpgCIEcTjkan63Cxux3L94RkE8C0ZGXXRd3GrqY4O2v3L2vIVVYqkEkRBSA7OS/iylVB0a8l9AVw8fArhNKf0EgP8ZwP9LCFkDkHc5yZWRKKW/QCn9FKX0U5ubm/P5H5gTjjLWyOaVnMFDBhoaDYCUy3AxlB76U8xCpFQSxKJqhEoQg0P9BLB1yzWbnDEoldgG0GuUQZWtxf7B6U87BweM9/k+H7swBqqCeAy+8+G7rsuXx7zIIEUeQQxDoSDKjc1pCeIke2kY8qpsv48QrhYyZBTE2UCI5IMiafSsCuLxMTsObRt4+unxv/ell4BPfYqfb09OMIQnQ05czxRqJiDPRm1ZAHEcwGYP9lBBFCbA/v6ZftekgBpAzkAEqOwltx2AWIYgzgDx900StpbUk5WxY9RnJoiUyiyFSceSmmBaxQlCoSC6Hiwrf+6lwXTkjrq4oRdrtl89ynnl+cHSCCIhxAUjh/+KUvpF5fsOgL8N4Avie5TSgFJ6wG9/FcC7AJ4HUwxvKj/2JoDZjdsrRvsg1u43r5lO72loNACUy/AwlKMugtkJYq/HKj6lEgr3faYn2OEQPdUxQAi2bpnm0XHwPMbJ3Cb7fAvl9+D49Fck0aZx69bk+ZU6QeRXT8cQxKKY2IM4h1mIkwjijii69nqw1Hgj1zUK4ikglitJhIKYCTqZkSCKgtnly8U2l4eHALpdGVDD35QhiOORRxCjiCtFWZvpGYNqJs1ABPj1j59LqennPjVcly1dkrB/4yxBnHEdT07YzyqX8xVnAZUg+gjkA3zEhSlwnw6+z4h5HLN1aLeBW8/oJ8S7d4Ixrz4fWFaKKQHwiwDeoJT+75mHfwDAm5TSe8rzNwkhNr/9NFgYzTcopQ8BHBNCvp3/zP8GwG8t4/9hbuj1cDRQPkSEGIJYAI0GgEoFHkLE4mM7DGTCWkHMai/9+teV3vHjY3ShXEU9H1tXzdl1HByHfdn1KgiSdHPRCywMjk534hQV00kkT7UoUkCOJeFX0Y2NU/3qC4VxFtPcHsQZCUYUsU0LIfkqvrAQo9dDCUpFxiiIp4KqILJxQZJ99FFGcH82BUqEAxdNsDzci4FeD0Mx4gIAPGPNnwRBEONYJ4uMIMrjbx59iJMUxCiS51ICKt07pp97ZjgOK9AkCTsmE+9sFtOZ+g8B4OQEPpSkVG7zNjgdCOGzgSFHXdz+qH5Su3vvfO8Pl6UgfieA/xrA9ymjK/5T/tjnMBpO858AeJUQ8nUAvwHgH1JKD/lj/x2A/wfAHTBl8XcX/u7nib29kREXjeb5/hAtA80mgHIZDiKpIA6HqRJRFEJxLEIQHz4E/s2/kfc/tvkIx1DOxibBdCKEvY3YNspWpCeZ/vXswn+/z07Enjfat6ZCtSjGcLSqd71eXDm+yBinIG5sAE5Flqu7qKL7YLYqTbvNyMraWr5SohJEfe6aIYingfi8Jwlbw+a6fr1pbc+WKjwzQdw+AUClgugwe6IhiOMhjoso0tWheh2joy7OQBCjiI1JsKz89VQTTOs4HplJaghicTgOJ4ZcQUzcTEjNggiiKKqi282MKfHM+p0RolAdRey6dv0TW1qf7s6hg+Aci4hLSUehlP4Z8vsHQSn9qZzv/SaYHTXv+V8B8E3zfH9LxU5mBqLnmrleBbC+DsD34SCWCmIco/2oD6C4AltUQez3gV//ddmAvLkJ/FjjNfwzKI1vJsF0KsReplxKkPQUgvjGLm5811Mz/awi/RaAMpaEz9GTFkVnbOqpgQ6VIIoRCVHE7m/d8nD/L+Rzd+4O8XQUFZ4/MclemoYQJQkQDGBDiVV0jMX0NFAVxDAE1jcdqJph+0EPV3NfmY9ZCGKSAC2e2p0qiB57oUkwHQ9BCgVBFH/zWg1zTTJV1cM8q6GaYLqGjnTQGII4M1yXFTZbLXbsxI6sci5FQcwSRM81x+AZsb7O1lUUpeMnn8EWXmGtFwBob4D798f3an/YsfQU0wuP3d0RBdFUxaej0QCIReB7QKRsGu+/eTLhVToGAzlUdtKFjVLgi1+UG1nfBz77WcDbvZce+OwBoyBOg9hElspaNxkO3pl9QJC40BUmiP2+LCYAgOua/sOCENbSLEEEgK2bnjbD61F0SU5FL4BJBHFvj2+G+31U0cVAJNA6DrPjm3PlzFAVxDAEmlu6hN6aMVVYkJUi5OD4GIg74oCk6YgLYPrG9iLDsthXkuijJ+ZNEAv1HwJAv48GjtADX3THEMRZ4TiyB9HzgMSW0vAx6sDuLorKTUHAjkPHmV5oUQmipc2xNAriWZGdhdhefwq31EELwQDbd4b5Lz4HMARx2djdlT1RAOAZ21QRVCrsZFgtxYgV4Xv7neL6/SE3KTcakxuz//iPgXeUgSs//uPcSvDAzECcFWIvU6pamsV0/73ZbG2AVBCnbSy7XTBW0+vJocAA4BiCWBSEsI2pbecQxC0AVbmz2MEW8MYbhX5uzNrRYFn56/jBB/xGr4fb2MaRKKZxxcIoiLND2LFTBfGGvisUQ+yLIAjYGrpuMcFYBNQAkONKOEE0FtPJEMRQJYjVKkYtpmcIqZmWYJpVEPsZi2lRm7EBW0ctpCax0sCoY9SZMTE9AU6G2iozLWQmHVPC+0hT8JAag9MjO+pi/6SE20295eLuK7MXwz8sMARx2djZyRBEoyAWQUoQa0AMOz3NbX+QO+VkBEEgRY5JU08++AD4oz+S97/ru4CPfITdHtzbZ1YQDqtkCMc0iM1pqe5qCuLhvd5MP6dopDfANz1hCCSxHApMLMC2jMV0Bogh64Igiirp1asAKnJnsYsrhQkiH02Jcjl/Y6P2H97GNhvDAKQbUnOunB1ZBXH9WokdDxytoKw0Kk3GzP2Hh0hlKlsMWefqs7G3TYYg4CpBrFSgpdDOy2I6bi3SELh+H2voSAWRh9QYglEcap8nIfz8x78RwmXXqrffLvSzZslSkCm0yl6J9wGb9TsbymXg0iV5bfzgA+DWTf0523/TGX3hOYEhiMvG93wP2s99K3DjBmMq9brpQSwAy2IXo0rNAoUyMuFAbjrHgVK2f40idjBfndBw8/LL8vbTTwPf933y/s4HA+25m1fIxFELBpIg+mslXUF8FI95RT663WKR3uK54kORDgV2XQDEEPoZIBREMYdbUxAVb9IjXC1MEKcRDJUgXsd9reeJEGNLPA0EQRQKYnOdaEm0s4y6mJUgtlpIWUhaIDIKYiHkBUXlKohz6kHMg6og1o2CeGaIv3M6k1Q5j3awNjNBnFYwS1NoT05gIZGOGn4MGovp2eB5wM2b7BoZx8B77wG3n2V/Wx8BnsM7uBZuT/kpH14sJaTGQGL4qU+jf0PetyxTSS2Keh3Yr1VRQQ8xbFaR7vdw9y7wwgvjX3f3Ljuhet7k5wG6w+Mzn9Fj/nce6KRm68YUpmKQbiD8ZglUTTE9HPOCMSiqHgL6DMR+puJtFMTimMli+vrvFPqZkwhGuy0VC294ghKUgozrFrJTGYxCVRCHQ27T9TwgYH/fQ2wwgvjcc1N/1lkUxLRAZHoQCyFPQSyXAXhu2hx8hAY7aLrdmeW8wUCG4IybZ6n2IGojElwXvj95Fq3BKAQhE32IqMgD6Rh1bBYgiKL/0LanXw/VBNMaTpQUWqPizwu3b7O1iCJWELv+UhNf+NefxSb2WEls838E8IMrfpengzm8l4zsYHez6SmOtTUAzSaq6MrNRrc7Mfzr+JhVdQDgxRcnD3budGSAhusC16/rj+/s6Qt19SlTPp2GVEHcqCJRQ2qOfck4CqBoQA2gK4gnmdQ9oyAWx2QFUW5GH+A66BtvSiY5AZMIxrZSaL3p7qCjWvFdk2B6WqgKYhRxW5q/HAVR7UFMw8U8szktgrxZpOzvTlKbadqucgoVcZp6COgKoqOOnHFcoz6dAuIznyT8klSeXUFUk9gL9R8CQLerp9B6JmRoXqjV2DVR2EwfNF/CFUEOAeDOnVW9tTPDEMQlIzvY3fTUFEejwf5T5QoiAKDX0zaWKuIYeP11tjG6eXP6gHRVPbx5U6/c4uQEO32l5E0Itp4yZ9dpEBtJr+KCKqltB3QdeFB8FmLRgBpAVxBPwBklJ4hGQSyOcQrilSuAv+alB8gRGjjo+oXCMoQdPG9joh7HT5DtTNqzCfM6LVxXJ/q1GrRKWQvrbOhrAcxCECnVFcQQ/Pjnm1NjMZ2MPAUxnf/qZwjiKYJqpvUfpvZEAFa/qwecuIYgngaCjKfptBkFsQhBLGovBSYRRE97PwanR6UCXLsmCeL7a98M/NzPAV/4AvDVrwKfz455Pz8wBHHJyCqIZtNTHI0GgFoNZTKUBDEMsf1GN/f5d+6wDU21WmwOjbZBfSLz4MOHIwmmV7aM9DsNYiNp2wDx9AHrwTvFvPlJwjYqhExXHSgF9vcB9HpIYEkF0WE9bNOKBAYS4xRE2waeeYZoKuLbeL5QH+IkgqEWaG7H78mAGsAoiGdANmwoSxBPoyAWIQe9HrO0otuFj2Bkc2oI4mTk9SCm7YdcAT5Cg9G2BSiIHSVbo97flfZEAHAdQxBPAVVBtG1oJ8Jj1Nk6nkwe3TUXgugagjgvlMss10IQxPe6V0D/8T8BfvIngW/5lmJJQh9SGIK4ZORZTA2KodEAiG2hXLe1+XZ3Xx9Niep2WVHcsoCXXirWK6FtUG9nHhwhiN7EsBsDBrGJoBSolHUL4sHrO4V+xvExf30lo+rm4P59mdpGQeCJvhmuQBWc5W6A8QoiADz/PLQ+xCIEMYrYRdSyRq3evR6bgQiwx2/23zEK4pwgCKIg+tUqRggifTBdQTzViAte3WmgjWEaGOXBsoy9bRryFERCOEn0mZQYwWHJoqcgiIVnIIYh1qIDmWDKZ+CY9ZsdgiDGMTvPUVhAiZHENCF9giVxOGTnyrz+w14P+J3fAf79v2cjFYFRgpjuYTwzNmheqFR0Z1KnI0eqnXeY7dKSYSymp0e1ykddrHuIOlKNuvuNCEmik0Cx2dzaKlYl6/XkSdWymMVUQ3YGou+ZGYgFIAqkSQJUagSxcuLcf/sQ1/NfpmEWe+lrr/EbvR6u4qHsA3Ad0384I7IKYqi0ID3/PGZWEFX1MNs7o/YRX78OuIc7aOGb5TcNQTw1BEEcKhkj1YYL4btIYOH4zfuYdnidqv+Qx+ungUOOA1gWajXTez8NeQpiGLJC6a7SQ3qEBqozEkQ+JhbA+OtjulfhMxDliAuWCG0I4uwQ47pEoSVJALtSAQZ9piACzGb68Y/nvl4IDGtro8fPf/gPwJe+xG7/+Z8zF9RA5HydnKCODu6DJyTyAtHIPsdgZlSr7M+5scGOT9dluRePQzuLURCXjGoVePZZFpbhOIYgzgJxcvXWa3CVhvnweICdjBglCOKkmYcqVHvp9eujCkd8/xH2oPwwz8eVKzO8+QsKNbWtUtdTXw+/0c55xSiKJphSynpOAQD9PjagsFHXfSxO2MvEvBXESf2HI+r9wYE+L9YxFtPTIo/or9/QWV7rrd2pP+dUIy64TCWVfGMvLQqhIKqFzzDke4Yzjrro9dhxXS6Pd2WoCaajBNEowKeB57Gli2P2b5Ig7UNMFcQJfYiCtOftG7NZDB98ALkv6nYRw1bGPnmo1cxxOA9YFtvPX7smbdvvv7/StzQ3GAVxyfiRH2FfADtBi4u2wXQIgohmEzW8Lx/odrG9zQ5QgF38ut3ZCPjE/kMA++8eaYPemw2qXqMNxkBVEEtNH+o47v17g9zXZFE0wfT+fXkBLQdtrYgA1zUK4owY14MIsGPEXaukf+EDXMLhaw8xqcWzaILp7dsA9vdHehBNMe10yPYghiHQvFHFPctKF7fVBp7Y25tYUTtLgqkMqGEE0RTXpmOSgqhefI7QmDmkpkiCadoO0+uhgSPpoHEMQTwtBEHs9RSCyJNMT8D9p2+9Nfb14/oPk0RvI1YObYZuVyqUAOC5uHnTqPjzwpUrbP/5yitsbd57j51vz/vf1yiIKwS38hsURNqD1myirM5I63W1DaZQDy9fLj6naWL/IYCdD3Qys3VleqS/gZ7a5jX0neXBozDnFTqiiG1Mi8wLTdVDAC/SN9BWCYZjFMRZMUlBdBzgmW8qAUQeYO8crPOEoHyMIxhhqAfa3r4eAe12pgfRMQriKZHtQQxDYH3D0iL222gCb7458eecZQZimibMe59u3Sr89i8sFqkgTus/DALFrt/vYxN72AevsHEF0cyxnB1ZBTGOkTLtaQricMiIvWWNFksPDmQLQL0O/OzPAt/7vfx5lOJW7y3dkeF5uHEDBnPC+jojiYTIdRL70PMMQxANzg1EDyJqNX1o73CIu2/307uz2kuHQ5nyTkg+QXx0P9bub90w4nsRqMOU/XV9N3JwiKmz84S9dFrPkmYvBfDS4GtyQwMYBfEUmKQgAsDzL1haTPtbeGGizXQcwbh3T/6OzU2gErQASqWC6DgAsYyCeErkKYjr69Aswi2sF+4hLaocqQpiS5B90/tUGHkKYjrH0ssoiI8ezTRXdlr/4Ve+IvvXLpFDPIX38EB0jHOCKBw7BsXheWxUSRyz26rFVOtBzLkuqvMPs4VvdUrNtWuMvH/v9zKi+DM/1cVPhb+AB6L/kFiAbRuCOEcQwgji1auPl83UEESDc4PUYmrbcGoldSoTtl9l3ot+n1VHbbt4Qtfdu3KDeuVKfoV8N9Ois/VkwTL6BYfnyYuZXfEBWxLr/aiJkebRDIS9dFq1+sEDab8plYCnO6/gAIpk6DpGQZwR0wjic89BC6p5B89NJBnjehBH7KUHB6CAVBBdk7h3FuT1IDab0BZi3griYMDX++QEDiJZrOEEMa8IZ6BjnILYaPBv8uPiCA22uFPOpSomWUyjCPjLv5T3v/PyW7BAFYLI3tj1IgljBhpcV1pLU4LIq6ipgthu5zoxJo23UO2lKnG3bWCT7sJGogTUsJAhQxDni81NvQ/xvfdW+37mAUMQDc4NXFcOCvYaZa0n8O4dVu48jb10Wv8hAOwc6gErW89M8TsaAMgQRBugfil97BAburc3B0UDajR76XMx7KNDXUF0jII4KyZZTAHghRdQOKgmDNnrbVsPgLp/H/jyl+X9J54AsL+PAUoyUMFx4TjFrY0GOixLKoiUKgqiQhAPsTGR3M864qLV4je6XayjhbvgjJCH1BiL6XTk/Z1Tcg+kKmJqHSxoM41jRvYJyT+mXn1VP+9+s8s+FynB4MTUEIzZQYgk5bbNLaYgQLmi9wjm2EwFQcwbjZZVEDXwwsE9cNmen4DN+s0XzSYrfEURO1++//5Ug9SHHoYgGpwbECKVJH+jBiga4uFujJMTWXgrai8FpvcfotvFo0Ap2xGCrWdN/FcRqPPOfB+Ifbkj2cflqQSxSEANpUq/DICP3mRenFRBdByAEEMQZ0Re75qKJ58EnLpcz11cQfvVTJQeR5769OqrwL/4F7IfyveBZ54BsL8/MgNxff38N/yvCunsPLBjZTjkEeyK+ruN2xMVxFnUwzgG/vRP+Z2TE1TQk+vpeXAcmBFBBZCXT5AqiEC6qClBLBhUoyr52SJqkrBxCQLf8R2A09rDCaqSwLisYGPOp6eD6KVXi2+olNFDBfcECc8QxDCU/YdZNw2lOkEcmc+8uwsKleAbgrgIEMJcNeUyO8b6fV3ZPY8wBNHgXEH0IVrrDWygJR/odXHnDiMUs9hLo0i/ruYqiA8f6jMQPQ9b18yhUxRiQ+M4AFUIYhdVBO+O39QEAdvMTlOPHj7M2EtruwjgyQ2N48KyzEiZWTFNQXRd4Knn9Xkw77w2RB7U/rUkAX7v94AvflH+zHIZ+OxneXX94EAniJ5JMD0rRJGGUkbIn30WWv/o23geyfvbkj1kUJQghiHwa7+mKPrdLtagDP/l4RgmnG06JlpMAaDECGJ6rLz7bqGfO8le+uabLPAEYOfST34SwMGBtJcCgOPi2rXiDh0DHYIgJkkqxqaBUT+FX8L/if8BR6/qhVPRf7i2Nvp3b7dlv2i5nKMw7u7iEBvSkeG5qFTylUiDs+Fx60M0h7jBuULah9ho6HPuur1URdrYKL4BefBAblI3NvKVKvrgIXah5LJ7/miVzmAsRMWTUqC2pp9yDt4an3qpqoeT1CNVPXzhBcBuH2T6D1mCqVGgZsO0HkQAeOFb9XL22ztrUhJUIHiH5wGf/7yuUmxuAv/gHwBPP82/kR1xYWYgnhniGEwStqG8ehVYa9pAiTG+AUrYxq2xCYpFCGIQAL/6q8A778jvfdvwT1GD8nnwXGMvLQjLYucsQvSAoXRjz1l/qiBOCRkSGEcQKQX+7M/k/W/9Vi5SZgmi65r+wzNA7DHiWFHS+VrGsPFF/G38l5//MXz+83LdRetMXqEsay8duc7t7Ej1EAA8z4y4WBAaDd1met77EA1BNDhXUAmij6E0mQYDvPVqAGA2e2mR/sOjO3uy+gagVCZTRy4YSIiLWhwD5Ybey3nwjaOcVzAIu/CkSmc2vfSjHwVwcJBJMDUBNadBnoKY7al47iO6vPs2ns+d4yUIxrvv6gTi+eeBv//3WXEmRVZBNDMQzwy1SHN0xDaHL74IrQ/xTbw4lmRMI4i9HvDLv6w7xr/nuxP88NEXcB9KZCnfnBoUg+OM2rzTY4FbhNNjZUaCmA2Leu89OW7GcYBv+zb+wMGBTjBcx9gTzwCVIH7mM+I41A+s3kmMX/gF4Jd+iRGN3V12zOYVpscF1KTY3c2snxlxsSgQAnzsY+x2t8vOh+d51rkhiAbnCtUqv7DZNrBWxzHWEMJBAoL3/+YYloWZyMDU/kMAO+90tPtb60NTfZsBYvMfRUBpPTML8W6+pS2OJUGc1K/08KEMxPB9rkIdjCqIpl9mdti2bmeidPRi9/zz0Haa44JqBMFQw/m+5VuAz31OG+nG8O67uoLoGQXxrBCkIkmkXe3FF6GFDL2JF8f2IU4acZEkwK/8ij7L8m/9LeAzH2+BxBHugkuGtg1YtkkwnQF5CbSpo4IvRhdVRLDZcVcgFWPciAtV1f/EJ5S5swcHeAiFeRgF8UwQxZo4ZkT8538e+Kf/lOAqFKbX6wOU4g/+gB1XlLKez1Jp9OdN7D8EgN1dGVADAJ5rCOIC8eyz7Njqdpn1Vz0vnjcYgmhwrlCpsK+rV4H1LR8xLJyghhPUsLsdzGQvTZKCCabv97X7V6/E+U80yIVQdMMQ8Jv6DvPgUZi7qdnbYxfQRmO8akEp8Id/KO+/+CJXl0cURNcoiKfAuDlsKp5+GrBrcoEe4So6L4/2QgmC0VFqLR/72Jg+pq99TR/qXK0ZBfGMEKpFkrCEyijiKbQK45s0x3KSgvjVr+pzZH/sx4BPfxqptLEtEkw9k2A6K4SCqFpM00HpJR+w2EF6hAZj/lNSMaKIbVotS1/LnR3ZwkgIXz+AnYTb7UwPomMI4hmgEkSRJvv9P7GGf7nxj/Df4+dBQAGaAMEA9+5Jm+I45X1igikw1mJqsBg0GszJ9NJLwE/8xPkO5DIE0eBcQexnSiXgm16MUEYfBBQxbOztUd2qNgWPHjH7BsAuuONUip17enzj1vUCOe8GKa7w9s0wBKxqSWMF+4OqkokvIUZ6TTq5/tVf6XbFT36S38gqiI47k+3YgIEQtlST+hA9D3jytk7w3/marrgPh3LEhbrUV65gFAcHwPvvSwWREKBWNQTxjPA8du4URKPTEdY2KSPdwbOIXh/tQZw04mIw0Is0n/mMchzu7OSmJ5rNaXHkJQlTKhRhMnMfoppgqhZ+VCfNiy8q18JWC6BUrqHjAMQyBPEMUAmiOLYAwH3hafxd/AZugI8r6fURBKyIXavlt1ocH8uWb88b454yFtOlghDgh36InQerVSWI6BzCEESDcwXNFnPzFq5iB3Ucw8MQ1qCfG6QxDqqb6qmnxjdt7+zq96/czvF5GIzF5iYjGkkCEEI0n8whNoA7d7TnBwHbl1jWGBIBRu5/7/fk/U9/WrEIGwVxbhAbVFXByOL5b9aPh7czLYhCfYpjRhYB6QQYwcsvA1D6qipVwLKNxfSMsG22yRRE4+iIWb83lXNZBAffeDuSO1aOSerhn/yJJB3NpqI8AcDODvawqaQneqjXTXriLOATejSlPYqUv2E1QxAnjCoBxgfUqNZvjTwcHCCEgz3wCpvDdrsmpO308H0+8okfZuL4Yn59YAs7eIBr2N9LEARsvGUR9fDq1fw9DN0xFtNlY2uLqbnj9i/nBYYgGpwrqJvK3qVbuI1t2EhQRQ+14UHRWcEA9GLrRz4y/nk7B3oJ6OqzJqFmFtTrsooWRQAty93JPi4zj5oCoR5eujR+WPRv/qa8wF67BnwXQcG/AAAgAElEQVT/9ytPyAmpMT2Ip0ORJNPnv0Nn328/WpNMEHID1Fec2pubYwoy/LOQKoh1dqwZBfFssG1GCgTRF2NhXviYr5W43wyfHoneG0cQDw+Zii/wAz+QOV53dqS9FDDpiaeAsHlnR11kg2qKKojjCKJIyQQyIW8HB3iEq6Dgi8b7uUf6hg0Kw/NYjVRcv9LJMpwgDuAjgI+jYwsnJ+x6OKlQKpBrL40itA4SDCALQZWGCf1aNGo1ZuE/78UwQxANzhU0gti4httQmgj7fdx9N38OWxZ7e/Ki6Lp8LtgY7HT0ndHWC+bsOgscR54obRuI63I0wgEuAV/+svZ8QRDHVan/3b/T1+7v/J1M3+nhYe6YC4PZMW0WIgA8/4kq4Mkd49v0WU0VFgRDHbE31vL7ta8BUBRE3jxnFMSzIU9BBMYkmWZUqHEE8fd/X25yb93iCcIqdnZGlAvTfzgbBOFWz2/aqIusgrgAgqiPuDD9h2eF4zCCKEK/sgqiB7aHiYYJ+n3ppsnD1ICa/X3cz8ywvHHTMkUag0IwBNHgXEEliMdDH9cu6Z637ZfHz9VToV5Hn3tugk+818NOqOxOCcHWi2a3OitEb6jjAFF5PEE8PmabGNdFbj/pG28AX/mKvP8jP4IRdZDuj1pMjYJ4OhRREJ95BiBVyR4e4DpOviZ72QQxPD6WrxlrvfnqV0ExShDPeyV21RAEURB9QRBZUI1kC3lBNXkEcXtbHy/zQz+UP38tTTAFAM8zCaYzYpyCmJ2FWHTUhdqDKDAYyD42x8mo9SMjLow9cR4QBD2OlcLZCy8gAYGHISgIkihBGLIUzHGjEqYG1JgEU4MzwBBEg3OFSkU2eQ+HwIPLH9Me336NXekGA+CP/oi1NOUlf6ubm0n20sH7j9CBJDS2Z+PSpjlsZoUge5YFJMqG9AQ1BK/dSUvbQj28cmV0w9nvA7/92/L+Sy+xOPYs+vtdzVLjlh0zt/KUKKIg+j7w5NVA+947fy4libwE01wFsd0G3n0XJ6ghBt8Z12ool42l7awQFlOx0Uwtpi9AG3XxHp7C4G/0nuAsqaAU+Lf/Vj7+sY+N6ZEaYzE1KI48BVHrQSyXAUKkgvjggWT/GYjh3batj0tQ1cNLlzJq1YiCaEZczAMqQUwVxGefxREa8BHAQgIkMSxC0W7nj0ro9+VxbNtjzqk5CaaGIBoUhdnpGpwrEAL84A/K+w+rz6GvkIG770cYDoFf/VVGEH/rt4BXX9V/Rqslvfu2nTo7crHzmq5Iblb7Y+0eBuMhLl5RBFTqLlCWm9I9egn42tdAKRsIDOTbZf7mb+TFtNFgcfojqgWl2DvU55xcvuoaS80p4TjTFUQAeP4F/f7v/74klf2+HNAukLuZyQbUVFlAjemXOTuyPYhiLep14MYtebxQELzzSlfep6MK4l//tT5QXev/VTFiMfWMxXRGCII4VkEkbF5FeswAY4NqhL00m2CqBtSMOC2yBNExBHEeEGNnNAWxXMbBtY+BgqAC9k2bhjg5GclxA6Crh1tbY8Z7mQRTgzPAbHUNzh2+6Zt47wyA2vU1tLCBhDfRtw4S/NIvAffuyef/wR/o6YuqC+eZZyarE4/ebGv3t5rBmGcaTIJKEOt1AGv19LHfxY8AX/4yDg+ZKlypyAuoiq9/Xd7+zu8cMx+x18PBUJELiYXLWwUHYxqMoIiCCADPf0aXhn7nnefwT/5RHwcHMr1U9KuVy3oPVKcD/O7vAq/89jYolICamuk/nBfyehDFmr74LXqc7JvvuumDQcBe43mSrKgD1b/jO8YHCAWPWtiBnFNDjHoxM6aG1ABApaLPDR1jM525/xAwCuKCIBwtmoII4PDmNyOAjzqOQZDAikMEAfDKK6M/Y2pADTBKEI3F1GAGGIJocO5ACPCjP8rdNVc2UUUXLayDAjjoeJoNEWCbITVtr6i9FAC++md97f71G0aKOg0aDdnn+eST0Bjgv8aPo/sXr04Mp9nfl6TftlmRIBd5Iy4umzU7LbI9iHljLgDg0z95E35VbeSl+Mv/v737jrOrrPY//lnTe3pIMklISCEFAoHQWygCAtIEbxAQFEVABbn6k2tBuIpdESvKFYWrKEUQpUm7gIUWpKdgekghdfpkkinP749n79l7n6nJnElmznzfr9e8Mmefc/bZM0/OnL32Ws96Hq/kqqv8iUxDQ3RPvIPptm1wxRXw3e/Ctb+fw2XczuOc4u8M/o8og9hz2dn+/ResVU9zczTvbN9DyhIRyDt15a2p/NTy0g0bojLwnBx/oaZdzrF2Y27U/RIYWZ6rUuGd1GUGEaCoeKcyiDsTILrNbZvUKMDouXYziMDWvabTSC6l1JCNa/2D+/LLbffRZYMawL2nElPZdQoQpV8qLYVTTwVGjGAfllFNGZsYQc2OPBYvctTXJ9dz+vvf/R/i6uoo0MjKCubgdMA5eO7NZPri0GN0hrMr4gFieTkMGh1lLeop4sHnhrSWOrXXwCReJjxlSgdr6AFs2ZLSwVRLXPRE6jqIHWUQR42C7130BoOJZdw3buTdd+GWW5JXwOPj+/TTsROdmlpWMJFHOc3fVgfTtAkzUfHgoLWT6YysxBvqHfZtDTLiZYngy0tD06Yl57IlVFaypjE20FnZjJvYzpo10qlw3OIloW0yiMU9yyB2VmK65b1GGoku/JSUZbVb3SE7Jx4gxjOIWwZPopE8SqmmiFpo9H9wFy5s20uhywY1QNWaGuqJ3tsFRVntNn8TaY8CROm3Zs3yi3RPK15LIduooxhwNDU0smULfOxjtC5vsH07PPdc8rNzwoROAg3gnXdg49bopCaf7Rx+Tke1HNKZsrIoQKyqgg9+pARi2YXfbzqRhk3VlJW1Pel0LlleesABnbxQagYxR0tc9ER3upiG9v/UsdzG5UwjyGBUVNDS2EhDA9x7b9TFNJ6lePLJ4JvmJthWn9gfJf5MVhnEngsDjfjfuzBAnDIFLHbHGsZS+9oSIMpuhPMX4wHi/sn+YEnqYJoW3c0gVjGI1vihgwCxvQ6mjY1RoxMz2vytXLcxGdSPGaNqjHQIG+21ySCWjKeRXEqoYyhbyWryU1oqK5PDumMHbNnivzfzcxDbE59qA1A+ukXz8aXbFCBKv2UGZ5wBJWNKOYAogiiinlWrfOlMvKHN/PnJJRJmzOh8/88+VA07ojmHh2W9QsEBnaQcpUOlpVF5W1UVfOC8fApLo7mBmxnO/Ic2tHt1c9Wq6GS2sLDzpkKsWpXMIOZpiYue6O4cRAD2248RU4fyI67h/TwGztG8xQ9cQ0N0ghMGiBs2xDLDNbXJfRUVQbY/OVWA2HNhoBEPDsLAoKAAJo5Nrh/7zksVQDKoWL06eh8WFXW+dmzbDqa56mC6CzrKIBYUxObOFxXSRA7bCCZlL1vmr4iSfM6OHW07mG7ZEr23hw6N/p+E1m5NTvQes7eywOkQDxDDDGJLC1QUl9NILlk4prCEou0V4PzVuaefjh737LPRuI0Y0fEyXWvXJ0/xy8drPr50nwJE6dfKyuDUo2qZyhJKqCWf7YzI2kxzM9x5py8hDa9ct7RE8y3MokY37XEOnns0mdE4bp93O1kwUTqTnR2d6O8IzkXP3H9l6/0N5PPbRwbzxBPR3KhQvDxx//076NYWe3Aig1hcrAxiD+xMBhEzOP988mjk//E9Psavad7io5DmZt+Jr6kpChDDEx4AamuYxDLO5C/k0piYo3rooen9mQaizjKIAPtOT76pFi/wHYXiAWK8zHvmzC7eh+pgmhZhwJYaIEIsi5idA/n5UZlpSwssWZLYT3fmH7a5kOYc66qT6wOVT2qvM5jsrHDs4hnEykpoLBxEc8lgiqllBospoaZ1AvfLL/vPxrvuguefj/bV2YWatVuS5Thj99EUGek+BYjS7x1wyijO449cwp3sxQayggjj8cf9Ve+TT277nHHj6HRtvCVL4L13o6vqeezg8CNUm9ET4QlIU5M/OT3/3GZyaKIFo5pBVFZn8/e/wx/+EJ0E7diRbCrUaXkptA0QS0raX1JBumWnMogA550H+OLhc3iAouoNtDQ14Vywbum6aMH21vJSgJoaTucRruUW7mYel57wLnPnwk03odLENAiDucLCtktdAOx76KDE499ZVcCOHf59GP4fWLAgur/T8lJ8cwyVmPZcRxlE6KJRTUqZ6S51MK2vZ11TbKNlMWZCXrePXTqWGiA6B1u3BmM7ZgzD2cxkllJAAznbfQS5bh389Kc+QRyaPBmOO66DF3GONdXJCaPl+2pBYOk+BYjS79nsA5nFW3yOm9mHFVBbAzicgzvu8Is4z5yZfE6X5aXPkkhlHcrLFM3p4knSqcGDo2Bj0yYYdvwsTuFxmshhB7lkNe7g7bf9vIkHHvAXwhcvjjKOw4fTeYv1lhbc628kS0xLSjUpvwd2KoMIPoKfNAmA7RRwKC/SUlXTeoK7fLk/2V2+HFaujJ6WVVPN8TwDwFAquOSKQm64oZMumbJTsrN9iXdhYbTcSGWsn9C045NzqxdXj6F+o//7V1zsL5iFnWgHD6bLbGDV6ipqiE5O8wuzdKFmF2Rn+/dL/CJNuwFicRGf4wdcy83cxieY/1RVoqnJrjSoaXeJC3XxTou8vGjKRVOTrwhuDRBHj2YkG5nMUgwobvCTDbduTXaDPvZY+PCHO1mmq6aGtU2xyYlZWZRPVgZYuk8BovR/U6ZAcTFZOD7Kb/xf3AY/B+PZZ/3JzYknJkuiOlvewjnf0CY+L+o4nutG+ko6E+9kunEjsP/+zMv7EzvIo5kcstwOKjY3sXatvwD+5JNtm9N0OsF+xQqqa40mgrqsnByKhuZ32ohIOpeaQexomQvwAcdzfzPWnPwxAGoo5Sj+CZXVrY/ZssUH/U89FXticzOH1D/HYGIprQMPTONPIeCDw5KSKMiPZxAnTc8jpzDKDm1iBGufXwn48tJ4c5pZs7p4HwJrViT/o5SPbFJzjF3U0VIzie6+RUVso5DXOZA/cAFfePAIvv/96O5dXQMxuch6jtZATKPwcymch7hlSzC2o0Yxig1MwqcKi7dvhpZmqqr8Y/PzYd48OOGEZPOiVG5DyhqIuXmMHac3oXSfAkTp/7KyWoO3Y/kbU1gSZBG93/7WT8A/+2zf7evUU1OuvqZYtgzWrYkmB+TQxJE8rwCxh+KdTDdvBnJzGTt7BBNZAUAWLdiO7a0noy+8EJXTmPkT0069/npK9rCEYcP0gdgTO5NB/OMf4Zln4E4uoYoyaihlGFvZu+7t1kYLubnw4IMpAWJtLe/jiej21KlRFwdJm8JC36AkHMuGhqiXSU4OTB5ZnXj8vx7zayFmZcG//x1t76q8FODdNcn33fhxroNHSldycpJLzYQB4jHHxB5UlBL51dfz6KM+6wRtlysBH2yEnTChbQaxdk1lIgucm2fKAqdROMUlLDNtzSAWFDBqShll1DCCTeSxg8LmOlpa/N/iT3yi8/4JoarlW4LO7l5+PqqmkZ2iAFEyw+zZgJ/7dBm3J8pDX3nFnxTtvz9ceSUcfnjnu3ruOaCuHoLG4YfyMkUT9lI7xR6KB4jhiUn9gUcylX8DjiwcJa6G2tpk6RPAxImdB/UAvP46S4nN2C8pUQfTHuruHMSqqqileuOIMfxtxHmtJ5fT3EKo96368vL83OD4+BY0VPpMY+igg9L9Ywg+ODBLdrGMl5nuPysZ1D36pH+zvvtuNO6jR7eTaWrH6o0pzTEmqrnXrgrXIk3NIB59NHzta/7zbNColNLBev/5tXChL9FvavL7iJcjVlRE+ywra1uquG5psknb6NI6ZYHTKMzmhhnErVuj6RRj5k4BaC0z3atlPWPG+IZd3f1MW7Ug2e2tfFCNxk92igJEyQxBgAg+oBvUsKH19rZtyflOnXGu7fxDlZemx6BB0byLMEDcOu1IAEbhxyu/oYrhw9uu39SdX3/Tq29yJ5dEG0pKmDChhwc9wHU3gxjPMGHGy5PmsYWh5NDIMCoo3u5TGe01AT664BUKiLXlP/jg9By8JBQGMUQ8EIiXmZ7+yeQ6FP9eX8KqpY0sXx5t6zKLD745xobkQI/bV3XeuyrMIKYGiGY+i/itb8GfHsnj94M/xVye9Xe2tEBDAwsW7GJ5KbBuVbJMePSQhrYPkl0WzyCGF0Wbm/24jvnAHIDWMlOrrCA/H1as6Hq/zsETT8BXbp+Q2F4+TOMnO0cBomSGWIBowIz6VxJ3xzthdmbFiiA4CQLE1vJSzYnqsXgGscIvs8bWvWezmeHMwLdIzK+vICvLUVkZtXgvLe18zmjooZdGJpoqZJWVcM456fwJBp7uZhBTuurTsM9M3uAAsmhhB3nsW/cqWa6p3aURTqp+ILlBAWKv6CpA3Pv9MzikyL8PHT7GeOrezaxd6+83g/326/p11r+wkle3xWrgsrMZf+ioHh37QNbRHMQ4yzJGzxzKMfw92lhXz8KF0Tp7qXOx41n89gLEtWuSZcHlIzuZgCw7LR4grl8fZQ8HDYL8E46C3Fwms9RvbNgGDdtYurTzfW7eDF/6kr9oUPteXeK+fWcqiy87RwGiZIaZMxOr/M6sfiHxSdrdAPG554JvgjmMB/MvSqhTBjENSkv9EGVl+ava9fWwJncCDdmlTGQFQ9lKbvM22NaAmf+gPOMMuPTSKPPYkfrVm7lzy+nRBjPOOK9Qa6/1ULiwdnhyun599H2osbHtle0dw0azrGgWG/Fd9Ka0LKJg3XKsPln2NLhoB3NW3Jd8cuxij6RPGCDm5kYBf7zElKwszpvrU/vN+Ej+5X9lt2agJk5MLE/ZruZm+OYN26knikaKB+cxcbIW6N5VqXMQO5wHPH06M4mtRVJfxzvvQHUwtbQwpQq10zUQgXUbkqeHY0ZrHmk6hdOsm5th7drodGX4cHy698gjWzOIAFRUsGwZie60cc8/7z8rX3wR/0e6sqL1vlm8ydnX7N0bP4ZksN0SIJrZODN7xswWmdkCM7sm2H6jma01s9eDr9Niz/mimS01s3fM7JTY9lODbUvN7L92x/FLP5Cfn1jLYgYLE2Wi8TW8OrJ9e9g8w7U+t7VkRxnEHsvK8ieYYRZx2TLYsCmb3BGDyaWZuTyHAdT4M5rHH/dD2p2F7u/+8cZooWigoCSXSy7L6eQZ0h1mMHJktH5eTQ2JkkPw5dvxk5t99oHtOww3eSrz8aVShTQwu2k+vPpq0DnDwfr1nPC3G8luiWUmJk3SXN9ekp3t/0wWF7ffyRTgkAunsjeraCabFoy66hYWL/b3dWdq6F13wdsLkxOdLjt+ecet+KVLHZWYtjF9OiPZyFCCzjT19WzfHmX3UzOIXZWYrt+cvCo3ZpyC/HRKzSCG47pXuDLFiSdSzloKCEpDKyqoqUmOW2jJErjxxqicmKoqaGkhjx1cxc/54ZjvUzynG2U4IjG7K4PYBHzOOTcdOBz4lJmFi8r90Dl3YPD1KEBw3zxgJnAq8HMzyzazbOBnwPuBGcAFsf3IQBcL4vblHSzWyXT1an9y2xHn4JZb/GK0NDRAczPZNPvmGWVlaDJbesTLTJcs8R92BaN8QPA+nqCM6taBqquDxx7rep9btsC9Dycvj//HrEXq2JYmubk+exSeoL7+evL++PzDqVPh+OODcqlZs7CJE1vvez+P+bOhN9+El+fDO4s5qfZPyZ1p4cNe1dlSFwB28vv4IA/QQhb1FENTI/9e2MSQIV2vHbtwIdx5J1AVdUM9jJc4+1IF/D3RUZOaNqZPxyDKItb5JjNh9Uw8g+hcF2sgAmsrk5MWx0xUlJ9OYdO15mb/FZaYjgqrsU88ESOah0hFJeBaL9iE6up8cJj4f7F1K/vxNrdzGefzR7JOeV/Xa9OIpNgtAaJzbr1z7tXg+xpgEcQXaGnjLOBu59x259wKYClwaPC11Dm33Dm3A7g7eKxIojStiG1MdMlUx6JFHT/1oYfgr38NbgTZw5N4ilJqu7EAn3RXPECsrPQnKYXj/NnJPqzgbB6MaqKA++9vW9KY6o47YHvlttbbg6nkQ2dt7/gJslOys2Hy5Ki0afHiaMFm59oGiMOH+26X2XnZ5Fx0ARzss4iH80Jw8uqgvo5pLGYasbOdCRPg+ut3y880UBUV+QxieDKZKDEFGD6ckw+pII8d1FGE0cL2qm1kZXW+5lp9Pdx0E7Q0NkGd//s5mEqu4zvYEV20jZZO5eR0PQcRaJ2oPYMgIqyvwznX+v6MB4hVVdF+wv8TcY2NsKkuSjkajtGTUx4kPRKWazc3+3/D8Rg9OnjAIYdAaWk0D7FxB9TVcfPN0fJPzsF3vxtc2I75hP2KH3ENYwkmEJ9yCiI7a7fPQTSzCcBs4KVg06fN7E0z+7WZhUu/lgPvxp62JtjW0XaRNnOXZtS+nLjd0TzEhQvhJz+JbaitZSxruJof+9sqL02bQYOiALGmxmf/Cib4S6blrOUs/kxO1dbWCGTdOj+3oiOrVsEjj5AoJ76UOyg6tBvdNKRbsrP9+llhGVpTU1SyvWlTlIUqKIBx4/y4zp4dzBvNyoLTT4dTTmUkm/lvbuB87uNcHuC/ucGXFBcUwA03+J1OntzOEUi6FBb6gCDMINbWRieoofzTTmQsa3BkYTjyttcyf37Hc5+amnz1xfr1QHVUpvFffJsh00Zp8bUe6nYGcfx4KCqKMohNTbRs287Klf4tFg/wuyovfeMNcI3RZMfhbCZvlMYxncIMYkuL/wrfk+XhGW1uLhx3XLLxUEUFVVVw7bXwzjvwpz/B3/6W3O+Zc6v58OpvkxUs04UZnHRSr/4skpl2a4BoZiXA/cBnnXPVwK3AJOBAYD3wg/Ch7TzddbK9vde63MxeMbNXNrVXtC2ZJyWQm7npWWiJzn7am4dYUQFf/Wpy4n9B/VZu4isUsa3d/cqui2cQ164NmtYMHcygMSWUUMdQKjiJJxOXRO+7r/19rV8P3/gGuObm1nKqsazhdB5RU6E0CjuPxjtYhmWm8ezhpEn+sdXVPovY2nnWjPzjDqf0z79jWPF2ruJWPsNPGckmOP98n5K88ca2k6Qk7QoL/RiFTZ+cSyTsAag99jSyaMFwGI5B29bx7uoWnn7az9MOrV4Nv/iFH8Innww2VvurBefyAIfxMhxxRO//UBkutUlNhwFiVhbMns1U/k02/nOvpbKaLVvaPqer8tJHHgGaoifN5rXuTQaXbisqiv62hhUZubkpv+YTT+RgXuUcglL8oP13TQ3853/Crbcm9zllCnxq/EPJjXPmaOxkl+y2ANHMcvHB4V3OuQcAnHMbnHPNzrkW4H/wJaTgM4Px/oNjgXWdbG/DOXebc26Oc27OiO6s7Cv9X1mZP0sNzHBvQ23U6nnRouRV8KYmf14arskX+q+s77E3q6MNCjbSpqwsaja7cWO0aPfYM6Ig/Hzu89Gf85fM33wTfvnLoLdJ4Omn4eMfDxow1PlFoQEu5zZyJoxTo5M0Ck9ipk6NshDvvuvfN/HlLab4tZ1b5/q+LzbtZfx4sDM/4FvsffjD/uuZZ+Dee2FvddfbXcIYPN40JrXM9Nnag8nJzWYM68hnB8XN1VBdwze+AaeeCuedBx/9KFxyCdxzT8rzq6qZwEou5zZ/WwFij7W3zEVH2VyOPpp8drSWJTZX+8qKcKmSUGcZxMpK+MffWxJXTU/jMRgyBEmf9gLE+FrBAJx4IgCf4Sf+c7GysvVzsb4+eWG7qMgXYuT9319JOPnkXvoJJNPtri6mBtwOLHLO3RzbPjr2sHOAt4Pv/wLMM7N8M5sITAFeBuYDU8xsopnl4RvZ/GV3/AzST8TKTMfxLqWNUVRRV+eveof+8AcffMTNO7Oe4967J9qQnZ3ojio9M2iQDxrCD8FwXkz5h45q7fu9Dys4uPGFxFnM3XfDvHnwwx/6NZ5uusl/QAKtS5LM5jWO5h/K+KZZeBJTUBAFgQAvveQDRfBjOmWKP3ENq30nT/Zx4NFH++VKAJ+GvOsu/zV37u76ESRQUBC9/8IgI96opr4eXngpG8rLmcoSBlPpy3ZiV2e2bPGda9tylNas5Xq+Tj5Bxw0FiD2Wk+PHLD4NvsOlLo4+Goga1bRU+TfjqlXJh3W2BuKTT0JTQ/QCY1nDrMGraXcRU9ll+fnRxdIwQGztYBrabz8YORIDruRWLmz+30QZd9x110H56BZ44onkHZp/KLtod2UQjwIuBk5IWdLiu2b2lpm9CRwPXAvgnFsA3AssBP4KfCrINDYBnwYexze6uTd4rIgXCxANmJ71TuLucB5ifb1PXqQ+9eMHzE9unDYtSnNJj4VrPw0b5r/CX2355EK/iFNgHne3uezd2Ah/+Uvbzz9qazmcF6M5bVpHL63C88Lm5mTsPX9+lNUYM8Y3uqiv948rKPDlUlOm+Okvgwa13a/sfllZ/sS0o06mzz8ftMovL2caiziOYGHYrVva7CuuuBjOPnIjtzV+lH0IFsUsK+u69al0KXz/xecQdlhmeuSRQNSopmWb78gdLk3jnK+kWb8+ekq8xNQ5ePhhYFtD67bTeQQbqSqsdDOLmgOFHUzbBIhmrVlEAy7jdi7N/m2bfZ17Lhx7LH7y6MaN0R2lpXC4mkTJrtldXUz/4Zwz59ys+JIWzrmLnXP7B9vPdM6tjz3nG865Sc65fZ1zj8W2P+qcmxrc943dcfzSj6TOQ6x+IXE7DBAfeSTR14TSUj8XMfvtNzrdn/RMSUl0khp2ccvKCjq3XXFF6+Pm8C8+W/XfDLKq9ncUyM2Fa8ru4Jt8yXecBY1ZmsUDxClToqxvvMxt6lT/b1he2tWC6rLndNTJdPFiXwHc2AiUlzOXZ7mem7iQuzio5m+MGtLQppnzAQfAl77kuw1fM+UxRrEhuvOwwzpvfSrdEmaZ4gm8DgPEoUNh5szWDGIz2VBfy/Ll/nrbnYvCwzsAAB07SURBVHf6suDw+YWF0UU78PP0V6+mdS3abJo5hcdh//3T+0MJ0LZ7bOsSF3Fnntn6rQGXvPIZPnnoa61vrTlzYh+dqVdPTzghmvQvspO0krRkltROpmufhPFfBfN/TRcs8FfOUxufnHNOMG0tdZE3BRtplZXlg4d41mLkyKDkdPp0v4jeM88AcBZ/4dQp1/PoqT/mnntgw4bkviZMgOu/3MI+B/00eYfGLK3iAWJOjj9XfDnZIFgBYj9SWJgMEKuq4J//hKee8kF/YyMMLS/kwP0dZW/V8HFu9w/84F40/ceFvPeeLzMdPdq/d1u9kLwYF2azpGfC9188OO8wQAQ45hj2WvALBlNBBYNpqaln/fpBfOc7yYayhYXwgQ8k9/vww8E3wRv5SJ5nCJVw6KFI+pWUJG+PGdPOg84/H37wA3jlldZN8+49l+OeWsDWhiKmT49dh3n88eRzVV4qPaDLe5JZRo9O1GlM3/E6tq2+9fbKlb5MMT5JPz/fB4iAL9GIU4OatItfsYZYW2+Aq65K3Jf/+99wzonV/O53PlMxbZoP5D/0Id9BcR9bEUUl4BspjBuHpE88QIS2b4nS0ujKtwLEvq+oKFliumKFn3fmnP8qKIDjjoPi0+cmn/jYY+TkwNix/v9AIjiEtuvRaP5hWoQZxHgiqK6u/ccCcPTRGDCVf7ODPLZWZVNbG1UeZmX55O7VVycrgOvq4NlngxtBa9vTecTfPuSQNPwkkioeIObktL/kCNnZvktbPBu/ciWj/+drzJwZ21xXB//4R/K5alAjPaAAUTJPLItYTD3j86LUk3Nw223Jh7///UH2cOtWePvt5J0KENOu0wDxrLNiKwXj64B/9ztycnxXzFtv9Ws/XXll0ImxvYxvah2c9EhqgDhmTPJEZsoU/ytvaYnKthUg9l2pGcR4qXB5OZx2mh/fnNNSTi4ffTSZ+o+rqmq7jtBhh6XvoAew8P1XUhKNVbzJTBtHH00jOWyjkK0MpWWHb3u6caN/r155pf/MC0vFQ63LmDQ3QX09I9nIIcz3b+6DD+6NH23Ai/+dzM3tZMnQgw7yEX3cD36QPF959tlkannSpERXd5GdpQBRMk9KmelMW5i4HV/Ly8xnowD42c+i2eIAEye2c5lceiq1YcnYsbEbublw+eXJB/z85x33dVdJcK9LDRDNkn0PwmsodXU+SCwqirIe0vcUFvqS7vbmE551ls8gFhXhM4Dx5WIqKuDLX25/py+/nHyPzpihpWbSKCfHX1gLf8WpSzPFvZc3ntsGX8dWfLRhtJDd3EBxMVx4YQdZKtqWl76fx/xi69Omtb2qJ2kRDxCHDOliuuDXvpa8mtrU5CcftrT4/xiPPpp8vMpLpYcUIErmSZ2HuOm5Dh96/PFBwqq+Hn784+SdsaYpkj7xc428vHYWav7EJ5IdGRYsaKd1aUABYq9LDRDBX9C+6CK/FmW4jGG44Lqyh31buNTFsGFRF9oTT4Szz46ujxUX46OSz3wm+eSf/9y3r02VOv9Q5aVplZPjL6yF49VRgFhZCbf/2thUfiAl1GA4iqinPHcztbVt17wMLVkSW9O02j/v/QS9AVVe2mvin4VdXosuLW17jvLPf/q0cHGxf2/GqbxUekgBomSelHKYGYvv7/ATdd684Jvf/CZZt1NWBp/8ZC8d4MAWTyyMGdNOo8Pycn+2GnfBBfDWW8ltS5f6D8g4BYhp116AaObXOYxnf8O3mNbT7tvMfBbxiCPgqKP8ovfHHOO3hyXCRUXBg7/4xWSZmnP+72LqQnwKEHtVdnYyQOyoxPSNN4Iqw/HjyaaFySxhLzaSXeMjw4UL23/eI4/EbtRUM4dX2Itg0qIa1PSaYcOiTH6ikqYj55wTW1Q2sHw5bNuW3JaT469+i/SAAkTJPPvsEywK5E1gJUXL3oo+XQNz5gQLfzc1wfe/n9zHlVdq8bZeMnmyjwELCvyJabs++9nk7YoKf0U0XNDrrbf8otAVFdFjCgt9OZSkVXsBYqrm5ig70eE8GukzwnmIBx0UZYC3b4+ad7UG+YWFbTMTr73my/FDLS1+fYw4BYhplZPjE0jhR1hFRfvvx9Yl8Pbem4N4lQN5g2yaoaoScG2miYLP/P/1r/ENNZzBw9FtZRB7zfTpMHOm/zzsVjxnBj/9aewKTgfOOUdlwdJjChAlM/3oR62pKQOm178Ca95NPOSCC4Jv7rvPtzcN5eXBNdfslsMciHJyfGniF77QyRz6o4/2C1PGvfeeX3X9wQd9m8XUdS+uuSZYL0PSqTsBYkWFP3ktK9MQ9Afh+WU88bBmjR/DESNSzj9PPjlWahH4ylf8E8A3yojXLg4erAs1aZad7eenhevmtbS0Xy4aBogtw0ZQVtjEJJb7eYRNTVBXz9/+1vZ9/Oc/x+bl79jBsO1rOZKgI21urhq19aKcHPjc5+DrX/fZxG7Ze2/4yU/aTiIuK/PTa665JnkBR2QXKUCUzHTggYlmJzNZACtXtX4STp0aTFV0Dr7zneRzL7kk2UlT0s6sG2to33hj2zlQK1b4q6PxzCHApZf6T1lJu+4EiGF5abdPcmSPCjtY1gcrADU2wrp1/vvx49t5wg9/mKyoqK2Fc8/1JQCpHS4PP7wbb27ZGWHTp3j5dmqZaVNT9D5sbM5i8H5j2Y+3MILONlVVrFmTXCpv+3Z44IHYTmqqOY8/kkNsTZv8/LT+LJLUrc/CVB/7mK+ieeABeOkl/5+hshJefRVuuaXjTkQiO0F/xSVz3XRT6yfqKTxOTst2WL4MM98HxQzf/CS+9qEZfP7ze+Z4JcnMf9hddFHnj7v6arj9drXO7CUKEDNPGCCGGcQ1a/z4Dh3aQZOhUaPgm99Mbps/36+7ljofUeWlaRe+B+NVg6nT6jdvjkpQi4shd9YMxrCeUwnqR6t8yvGOO6JmRI8/nsxEFm3bygd4KNqg+Yd918yZ/mLpoYcmJzOKpIkCRMlcw4b5IBEYw3p+wRV8dMO3ufkjrzNnTvCY1Ozhuef69KL0DVlZ8Otfwwc+0P7911/vg0hlLHpNVwFiTY0/4czPTy78LH1XWEJaX+/ju7Vr/e1wPmK7PvnJrgOG/Hy4+OK0HKNEwmtf8SRuaoDYOv+QIJDcf38K2cal3EEOTa1rWG7a5MtKW1rg3nuT+zgz768UUx9t0PxDkQFLZ1WS2S6/HGbNAmASy/kIv+XAL53my6D22w+eeSb5+Ouu2wMHKZ3KzYV77vHzDuO+/32/NpSunPaqrgLErVv9v8oe9h95ef6aSmMjrFrlg8TBg7voy5WdDb/8ZfuLte27L3z6037ZmYkTe+24B6r2MoipJabxKdklJcC0aRTmNDGSTZzNg9DQ0DrF4q67fPFMeGEAICfH8cH1P03uVAGiyIClmizJbDk5fkJ3PLhYv95/pTr+eH0g9lWFhX4l5xtvhEWLfOB/1ll7+qgGhKws/9XS4r9Sk7UqL+1/wqUu6uqiXjOdZg9DBx4ITz7p/6YWFMAJJ/jGUe1OXJR0CTOInZWYxjOIxcVAfj5Fs/eF+Wu4kLt4hNPZVlUJI/eiqgp+8IPk89930FaGP7Uk2lBSomZDIgOYAkTJfMce67vw3X13549T9rBvKylpuxyJ7BbZ2T44bG5OBog7dvg2+VlZyfUtpe8rKvIBonM+8Oj2+pXHHdc2my+9KswgFhb6YLGpyfcJamjwcTokA8SwhLjw2ENg/tMMporzuY//rZoMI/cC2k4d/Y9xzyc3HHxw9MIiMuCoxFQGhptv9usjticnB6691rdzF5E2OiozDctLhwzRuWR/EzaqgW5mD2WPCTOIziXXGQ2ziA0NrVMMcc4HiLm5kHvcka2P/Q/uoWzDUmhqbLP/I46AvVc+l9yoBjUiA5oyiDIwjB7tu5W+/LI/yy0p8XU4JSW+JXS7rftEBDoOEMMT1PhJq/QPYZappETlwX1d+P5ravJjFWYLt2zxi6ynNqjJzg4uABx/vE/tV1ZSxDYubLqDW1fvC/skF6CdNw/49MvJF9V0C5EBTQGiDBwlJX7OjIjslDCD8e67vslvOCdRDWr6r5EjfeZp5Mg9fSTSlfD919ycfK+FF2jiAWLYaKioCP+Zd9118MUvAnAWf+a+NfPYXD62dX3DGTNg/+lN8K9/JV9UAaLIgKYSUxER6dTee/ug8L334LXXfDPEqip/wlpcHM2Dkv4jKwsmTIgyidJ3xTOIw4dH28NOpvEAMSyGaS0hvvpqX0ED5LODT7T8AlatbH38Rz4CtniRX/MkNGKE6o5FBjgFiCIi0qmhQ+Ggg3wgWFPjkw2rV/v7lD0U6V1hBjEsMQ2FGcT4EhfFxf7f1gCxqAi++tXW+0/mST7/3v9j7v5b+PKX4bDDgPnzky94yCFaPkhkgFOAKCIiXSop8Y0Nhwzx3UsrKvx2BYgivauzElPnkhnEMDBMZIYvuwwmRfMOT295iBs2foqTTsJf8bn//uQLqkGNyICnAFFERLolNxdmzYJx4/ztvLzk2mwikn7h0jLNzT4ADIO/xkZYtw62bfO38/KiYDLepZbcXLjppuRO77nHz02cNAkefTR5n+Yfigx4ChBFRKTbzPw55ezZft10VaKJ9C6zjrOICxdG3w8d6jOK+fntLDvzoQ/5N23ct78NmzYltxUWwpFHIiIDmwJEERHZaYMGqcGJyO6SutRFKB4ghtn8RPYwlJUF3/pW5y8ybBjcdZdfGkNEBjQFiCIiIiJ9WDyDGO9kGs4FhqiDaYcXbk4+GebObbu9qAiuvx6WL4dzzknH4YpIP6cAUURERKQP6yiDGFdS4v9tN4MIvlb1lluiB+bkwFVXwbJl8LWvaUKxiLTK2dMHICIiIiId6yiDGFdU5BvWdFr6fcABsGABvPgiHH44jB+f9mMVkf5PAaKIiIhIHxbPIA4d6pOBzkX3l5REtzvMIIbGj1dgKCKdUompiIiISB8WzyDm5LTtIzN8ODQ0+MCxoGD3H5+IZBYFiCIiIiJ9WDyDCG3LTIcM8f8WFETrJoqI7Cr9GRERERHpw+IZRGjbqCbsYNpleamISDcoQBQRERHpw1IziB0FiFqbVETSQQGiiIiISB+WmkGMl5jG5x0qgygi6aAAUURERKQPCwPExkb/78iRUVZx5MgocFQGUUTSQctciIiIiPRhxcX+3+rq6PZZZ8HixXDkkbB6td+uDKKIpIMCRBEREZE+rKgIcnNhxw7Yts0HgrNm+a/mZli61Hcvzc/f00cqIplAJaYiIiIifVy49mFlZXJ7fb3/t7DQz0cUEekpBYgiIiIifdygQf7fqqrk9m3b/L8qLxWRdFGAKCIiItLHdZVBVIMaEUkXBYgiIiIifVxxse9m2tDgv0LKIIpIuilAFBEREenjzNovMw0DRGUQRSRdFCCKiIiI9APtlZnGm9SIiKSDAkQRERGRfiA1g9jYCE1NvvQ0L2/PHZeIZBatgygiIiLSD5SWQna2zxqGayKCsocikl7KIIqIiIj0A2ZQVua/r6rS/EMR6R27JUA0s3Fm9oyZLTKzBWZ2TbD9e2a22MzeNLM/mdngYPsEM9tmZq8HX7+I7etgM3vLzJaa2Y/NtCysiIiIDAzxeYiafygivWF3ZRCbgM8556YDhwOfMrMZwJPAfs65WcC/gS/GnrPMOXdg8HVFbPutwOXAlODr1N3yE4iIiIjsYfF5iCoxFZHesFsCROfceufcq8H3NcAioNw594Rzril42IvA2M72Y2ajgTLn3AvOOQf8L3B2Lx66iIiISJ9RVgZZWVBbC9XVfptKTEUknXb7HEQzmwDMBl5KuetjwGOx2xPN7DUze87Mjgm2lQNrYo9ZE2xr73UuN7NXzOyVTZs2peXYRURERPakrKxoHuL27f5fZRBFJJ12a4BoZiXA/cBnnXPVse1fxpeh3hVsWg+Md87NBv4T+L2ZlQHtzTd07b2Wc+4259wc59ycESNGpPPHEBEREdljwjJT8Mtb5KgnvYik0W4LEM0sFx8c3uWceyC2/RLgDODCoGwU59x259yW4Pt/AcuAqfiMYbwMdSywbvf8BCIiIiJ7XtioBpQ9FJH0211dTA24HVjknLs5tv1U4DrgTOdcfWz7CDPLDr7fB9+MZrlzbj1QY2aHB/v8CPDn3fEziIiIiPQFZWV+yQvQ/EMRSb/dVZRwFHAx8JaZvR5s+xLwYyAfeDJYreLFoGPpscDXzKwJaAaucM5tDZ53JXAHUIifsxiftygiIiKS0bKzobTUN6lRBlFE0m23BIjOuX/Q/vzBRzt4/P34ctT27nsF2C99RyciIiLSv4wZAw0NMGzYnj4SEck0mtYsIiIi0s+MGuW/RETSbbcvcyEiIiIiIiJ9kwJEERERERERARQgioiIiIiISEABooiIiIiIiAAKEEVERERERCSgAFFEREREREQABYgiIiIiIiISUIAoIiIiIiIigAJEERERERERCShAFBEREREREUABooiIiIiIiAQUIIqIiIiIiAigAFFEREREREQCChBFREREREQEUIAoIiIiIiIiAQWIIiIiIiIiAihAFBERERERkYACRBEREREREQHAnHN7+hh6nZltAlbtwlOHA5vTfDiye2kM+z+NYf+m8ev/NIb9n8awf9P49X99ZQz3ds6N6OpBAyJA3FVm9opzbs6ePg7ZdRrD/k9j2L9p/Po/jWH/pzHs3zR+/V9/G0OVmIqIiIiIiAigAFFEREREREQCChA7d9uePgDpMY1h/6cx7N80fv2fxrD/0xj2bxq//q9fjaHmIIqIiIiIiAigDKKIiIiIiIgEFCCKiIiIiIgI0M8CRDMbZ2bPmNkiM1tgZtcE24ea2ZNmtiT4d0iwfZqZvWBm283s8+3sL9vMXjOzhzt5zb+aWWXqY8zs02a21MycmQ3v5PkTzeyl4NjuMbO8YPuxZvaqmTWZ2Xm7+jvpbzJsDP/TzBaa2Ztm9rSZ7b2rv5f+IsPG7woze8vMXjezf5jZjF39vfQnmTSGsfvPC/bRb1qI90QmjaGZXWpmm4L34etm9vFd/b30J5k0hsF9HzL/ebjAzH6/K7+T/iSTxs/Mfhh7//3bzCp39ffSn2TYGI4PfpbXzJ+Tnrarv5dQvwoQgSbgc8656cDhwKfMn9T9F/C0c24K8HRwG2ArcDXw/Q72dw2wqIvX/B5wcTvb/wmcBKzq4vnfAX4YHFsFcFmwfTVwKZDxf0hTZNIYvgbMcc7NAv4IfLeL/WSCTBq/3zvn9nfOHYgfu5u72E+myKQxxMxKg+N7qYt9ZJKMGkPgHufcgcHXr7rYT6bImDE0synAF4GjnHMzgc92sZ9MkDHj55y7Nnz/AT8BHuhiP5kiY8YQ+Apwr3NuNjAP+HkX++lSvwoQnXPrnXOvBt/X4AeiHDgLuDN42J3A2cFjNjrn5gONqfsys7HA6UCnH0bOuaeBmna2v+acW9nZc83MgBPwwUPqsa10zr0JtHS2j0yTYWP4jHOuPtj+IjC2s31lggwbv+rYQ4uBAdGxK5PGMPB1fIDf0Nl+MkkGjuGAk2Fj+AngZ865ivBYO9tXJsiw8Yu7APhDZ/vKFBk2hg4oC74fBKzrbF/d0a8CxDgzmwDMxl813ss5tx78gAMju7GLW4Av0LsB2jCg0jnXFNxeg//PJ2TcGF4GPNaLx9HnZML4mdmnzGwZPsC4uhePo0/q72NoZrOBcc65Dkt6Ml1/H8PAB4OyqD+a2bhePI4+KQPGcCow1cz+aWYvmtmpvXgcfU4GjB8A5qfJTAT+rxePo0/KgDG8EbjIzNYAjwKf6emL9csA0cxKgPuBz6ZkAbr7/DOAjc65f6X94FJeqp1tAyJL0ZVMGkMzuwiYgy8dGBAyZfyccz9zzk0CrsOXaAwY/X0MzSwL+CHwuV5+/T6rv49h8O9DwATnS/WfIrpyPyBkyBjmAFOAufgM1K/MbHAvH0+fkCHjF5oH/NE519zLx9KnZMgYXgDc4ZwbC5wG/Db4jNxl/S5ANLNc/EDe5ZwL66Q3mNno4P7RQFflDUcBZ5rZSuBu4AQz+52ZHRabqHvmLh7f48HzfwVsBgabWU5w91jSkPbt7zJpDM3sJODLwJnOue278nr9TSaNX8zdDKCStwwZw1JgP+DZ4BgOB/5iA6dRTSaMIc65LbG/nf8DHLwrr9cfZcoY4jMZf3bONTrnVgDv4APGjJZB4xeaxwApLw1l0BheBtwL4Jx7ASgAOmx20x05XT+k7zAzA24HFjnn4g0l/gJcAnw7+PfPne3HOfdF/IRqzGwu8Hnn3EXB3Qf25Bidc6ekHPMzwHn4/zRdHlumy6QxNF/e9kvg1IEw5wIybvymOOeWBA87HVjCAJApY+icqyL2AWhmzwbH8EpPXrs/yJQxDLaPDsu5gDPpuslDRsikMQQeJMhgmO/AOBVY3pPX7usybPwws32BIcALPXnN/iTDxnA1cCL+PTgdHyBu6slr45zrN1/A0fh06pvA68HXafi63KfxJ3hPA0ODx4/CX9mqBiqD78tS9jkXeLiT1/x78EveFjz/lGD71cHtJnwE/6sOnr8P8DKwFLgPyA+2HxI8vw7YAizY079fjeFOj+FTwIbYz/GXPf371fjt1Pj9CFgQ/AzPADP39O9XY7hzY5jymGfxXYX3+O9YY9j9MQS+FbwP3wjeh9P29O9XY7jTY2j4LtALgbeAeXv696vx6/74BffdCHx7T/9eNYa7/B6cge+E+kbwc5zc09+PBTsWERERERGRAa7fzUEUERERERGR3qEAUURERERERAAFiCIiIiIiIhJQgCgiIiIiIiKAAkQREREREREJKEAUEZEBx8zGm1mtmWXv6WMRERHpSxQgiojIgGBmK83sJADn3GrnXIlzrnk3vv5cM1uzu15PRERkVyhAFBEREREREUABooiIDABm9ltgPPBQUFr6BTNzZpYT3P+smd1kZs8H9z9kZsPM7C4zqzaz+WY2Iba/aWb2pJltNbN3zOxDsftOM7OFZlZjZmvN7PNmVgw8BowJ9l9rZmPM7FAze8HMKs1svZn91MzyYvtyZnaVmS0J9vd1M5sUPKfazO4NHx9mKM3sS2a2OciYXrh7fsMiIpIpFCCKiEjGc85dDKwGPuCcKwHubedh84CLgXJgEvAC8BtgKLAIuAEgCPaeBH4PjAQuAH5uZjOD/dwOfNI5VwrsB/yfc64OeD+wLihtLXHOrQOagWuB4cARwInAVSnHdSpwMHA48AXgNuBCYFyw/wtijx0V7KscuAS4zcz23alfloiIDGgKEEVERLzfOOeWOeeq8Nm+Zc65p5xzTcB9wOzgcWcAK51zv3HONTnnXgXuB84L7m8EZphZmXOuIri/Xc65fznnXgz2sxL4JXBcysO+45yrds4tAN4GnnDOLY8d5+yUx1/vnNvunHsOeAT4ECIiIt2kAFFERMTbEPt+Wzu3S4Lv9wYOC8pCK82sEp/RGxXc/0HgNGCVmT1nZkd09IJmNtXMHjaz98ysGvgmPgO4K8cFUBFkK0OrgDEdvb6IiEgqBYgiIjJQuDTt513gOefc4NhXiXPuSgDn3Hzn3Fn48tMHicpZ23v9W4HFwBTnXBnwJcB6cGxDghLY0HhgXQ/2JyIiA4wCRBERGSg2APukYT8PA1PN7GIzyw2+DjGz6WaWZ2YXmtkg51wjUI2fZxi+/jAzGxTbV2nwmFozmwZcmYbj++/gOI7Bl8Pel4Z9iojIAKEAUUREBopvAV8JSkLP6+rBHXHO1QAn45varAPeA74D5AcPuRhYGZSMXgFcFDxvMfAHYHlQmjoG+DzwYaAG+B/gnl09rsB7QEVwXHcBVwSvKyIi0i3mXLoqbkRERGRPMbO5wO+cc2P39LGIiEj/pQyiiIiIiIiIAAoQRUREREREJKASUxEREREREQGUQRQREREREZGAAkQREREREREBFCCKiIiIiIhIQAGiiIiIiIiIAAoQRUREREREJPD/AceGfoH9ww3uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "for t in range(1, HORIZON+1):\n",
    "    plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "ax.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean up model files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in glob('model_*.h5'):\n",
    "    os.remove(m)"
   ]
  }
 ],
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